Metabolomic analysis of umbilical cord serum in small-for-gestational-age infants with a focus on linear growth

Metabolomic analysis of umbilical cord serum in small-for-gestational-age infants with a focus on linear growth | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metabolomic analysis of umbilical cord serum in small-for-gestational-age infants with a focus on linear growth Masanori Inoue, Kazuhito Sekiguchi, Shintaro Kishimoto, Tomoki Maeda, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6525025/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Small-for-gestational-age (SGA) infants exhibit considerable heterogeneity in growth trajectories and metabolic outcomes; however, the metabolic basis underlying distinct phenotypes remains poorly understood. In this study, we analyzed umbilical cord serum samples (term infants, n = 66; SGA infants, n = 18; controls, n = 48), using gas chromatography–mass spectrometry to explore nutritional metabolic profiles. A metabolomic analysis revealed that SGA infants had significantly lower levels of metabolites involved in galactose metabolism, lactose degradation, and the glucose–alanine cycle, indicating altered carbohydrate metabolism and energy homeostasis. Among SGA infants, those with both low weight and short length at birth (SGA-short) had significantly reduced glutamine concentrations in comparison to those with preserved length (SGA-tall). The SGA-short group also had a higher proportion of primiparous mothers. Glutamine is essential for fetal growth, particularly skeletal growth, and its deficiency may exacerbate linear growth impairment in the context of restricted intrauterine nutrition. These findings highlight the importance of metabolic subclassification in SGA infants and suggest that glutamine-related pathways could serve as potential biomarkers or therapeutic targets for infants at risk of postnatal growth failure. Health sciences/Medical research/Paediatric research Health sciences/Health care/Nutrition Cord blood Glutamine Infant Metabolomics Small for gestational age Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Small-for-gestational-age (SGA) infants, defined as those with a birth weight below the 10th percentile for gestational age, are known to be at increased risk for a range of short- and long-term health complications, including metabolic syndrome, insulin resistance, and impaired growth trajectories 1 . Considerable heterogeneity exists among SGA infants in terms of postnatal growth patterns and metabolic outcomes, which suggests underlying differences in intrauterine growth pathways and biological profiles 2 , 3 . Recent advances in metabolomics have enabled comprehensive analyses of metabolic profiles, providing novel insights into fetal metabolic adaptations to intrauterine growth restriction. Studies utilizing umbilical cord blood, the placenta, and maternal serum have begun to elucidate these adaptive mechanisms 3 . Several investigations, including those involving term-born infants, have reported metabolomic signatures associated with SGA 3 . However, few studies have stratified SGA infants based on birth body proportionality, such as birth length Z scores or ponderal index, despite evidence suggesting that these anthropometric characteristics may reflect distinct etiologies and differing risks of persistent postnatal growth failure. In this study, we conducted a comprehensive metabolomic analysis of nutritional metabolites in umbilical cord serum samples from term infants using gas chromatography–mass spectrometry. By subclassifying SGA infants based on birth length Z-scores, we aimed to identify metabolic features associated with disproportionate growth patterns and to explore potential biomarkers that may reflect the intrauterine nutritional status or predict future growth trajectories. Results Perinatal characteristics To identify the factors involved in the growth of SGA infants, we investigated the perinatal characteristics and nutritional metabolite profiles of umbilical cord serum. Since preterm infants often receive treatment for threatened preterm labor immediately before birth, and such treatments are known to acutely affect fetal metabolism and alter the profile of cord blood metabolites 4 , we limited our analysis to term infants born at or after 37 weeks of gestation. During the study period, 462 neonates were admitted to the neonatal intensive care unit (NICU) or growing care unit (GCU) of our hospital, of whom 267 were born at term (Fig. 1 ). Residual cord blood samples obtained during routine clinical care were available for 81 infants. After excluding 15 cases due to multiple congenital anomalies or chromosomal abnormalities (n = 5), severe anemia due to twin-to-twin transfusion syndrome (n = 1), or lack of parental consent (n = 9), 66 infants were included in the final analysis. Among them, 48 infants with birth weights above the 10th percentile were designated as the control group, and 18 infants with birth weights below the 10th percentile were classified as the SGA group. As our institution serves as a regional perinatal referral center, we receive many high-risk pregnancies from local obstetric clinics, including those complicated by fetal growth restriction and maternal comorbidities. Consequently, the proportion of SGA infants in our cohort was higher than that of the general population. The main reason for admission in both the control and SGA groups was transient tachypnea of the newborn, accounting for approximately 70% of the cases in both groups (Supplementary Table 1). According to our institutional protocol, infants with birth weight < 2300 g or with both weight and length below the 10th percentile are admitted to the NICU or GCU for close observation and management of respiratory status, glucose levels, and infection risk. These criteria accounted for 89% of the admissions in the SGA group. Other reasons for admission included transient neonatal hypoglycemia, suspected infection, and meconium aspiration syndrome. Table 1 summarizes the perinatal characteristics of the SGA infants. As expected, birth weight, length, and head circumference were significantly lower in the SGA group than in the control group. No significant differences were observed between the two groups in terms of sex, gestational age, rate of twin pregnancy, Apgar scores at 1 and 5 min, umbilical artery pH, or the incidence of marginal or membranous umbilical cord insertion. Regarding maternal characteristics, the SGA group had a significantly higher proportion of multiparous women and a higher incidence of hypertensive disorders of pregnancy than the control group. Table 1 Perinatal characteristics of term infants in this study Control group n = 48 SGA group n = 18 p Characteristics of infants Male, n (%) 23 (48) 12 (67) 0.174 Gestational age, median (IQR), weeks 38.1 (37.4–39.4) 37.8 (37.3–38.8) 0.735 Birth weight, median (IQR), g 2963 (2651–3530) 2268 (2087–2344) < 0.001*** Birth length, mean (SD), cm 49.2 (2.3) 45.9 (1.3) < 0.001*** Birth head circumference, mean (SD), cm 34.2 (1.4) 32.4 (1.2) < 0.001*** Twin, n (%) 6 (13) 3 (17) 0.660 Apgar score 1 min, median (IQR) 7 (6–8) 7 (6–8) 0.692 Apgar score 5 min, median (IQR) 8 (8–9) 9 (8–9) 0.538 Umbilical artery pH, median (IQR) 7.28 (7.26–7.31) 7.29 (7.22–7.32) 0.795 Marginal or membranous umbilical cord insertion, n (%) 12 (25) 4 (22) 1.000 Characteristics of mothers Maternal age, mean (SD), years old 33 (5) 34 (5) 0.286 Maternal weight at delivery, mean (SD), kg 66.7 (13.9) 62.3 (11.9) 0.236 Pre-pregnancy weight, median (IQR), kg 56.5 (46.8–75.0) 52.5 (48.5–62.9) 0.530 Maternal height, median (IQR), cm 158 (154–163) 158.1 (153–162) 0.327 Pre-pregnancy BMI, median (IQR) 22.6 (19.5–27.9) 22.4 (19.5–27.3) 0.607 Gestational weight gain, mean (SD), kg 7.3 (5.5) 6.6 (4.6) 0.646 Maternal alcohol consumption, n (%) 1 (2) 0 (0) 1.000 Maternal smoking, n (%) 2 (4) 0 (0) 1.000 Nulliparity, n (%) 24 (50) 14 (78) 0.042* Caesarean delivery, n (%) 38 (79) 15 (83) 1.000 PROM, n (%) 5 (10) 2 (11) 1.000 Gestational hypertension, n (%) 2 (4) 4 (22) 0.043* Diabetes mellitus, n (%) 7 (15) 2 (11) 1.000 PROM, premature rupture of membranes; Diabetes mellitus, gestational and pre-gestational diabetes mellitus; SD, standard deviation; IQR, interquartile range. Correlation between weight and length Z-scores in SGA infants To examine the relationship between birth weight and birth length in our cohort, we performed a linear regression analysis using Z-scores derived from standardized reference data. Across the entire term population, a significant positive correlation was observed between birth weight and length Z-scores (R² = 0.64, Fig. 2 A). We then conducted subgroup analyses based on the following birth weight categories: appropriate for gestational age (AGA; 10th–90th percentile), large for gestational age (LGA; above the 90th percentile), and SGA (below the 10th percentile). A moderate positive correlation was observed in the AGA group (R² = 0.37, p = 0.0001; Fig. 2 B). No significant correlation was found in either the LGA or SGA groups (R² = 0.21 and 0.064, respectively; Fig. 2 C and 2 D). Although both the LGA and SGA groups lacked statistically significant correlations, the R² value was lower in the SGA group (0.21 vs. 0.064), indicating a more pronounced dissociation between weight and length in these infants. This observation led us to hypothesize that the SGA group may include two distinct subgroups: one with relatively preserved birth length despite low birth weight and another with reductions in both parameters. To explore this possibility, we subdivided the SGA group based on birth length Z-scores using − 1.28 (corresponding to the 10th percentile) as the cutoff value. Infants with a birth length Z score above − 1.28 were categorized as the SGA-tall group (n = 11), and those below − 1.28 were categorized as the SGA-short group (n = 7) (Fig. 1 ). No significant differences were observed between these subgroups in terms of sex, gestational age, birth weight, head circumference, rate of twin pregnancy, Apgar scores, umbilical artery pH, or frequency of marginal or membranous cord insertion. However, a higher proportion of primiparous mothers were observed in the SGA-short group than in the SGA-tall group (Table 2 ). Table 2 Perinatal characteristics of small for gestational age infants classified by short stature status SGA-tall group n = 11 SGA-short group n = 7 p Characteristics of infants Male, n (%) 7 (64) 5 (71) 1.000 Gestational age, mean (SD), weeks 38.1 (0.8) 38.4 (1.1) 0.397 Birth weight, mean (SD), g 2249 (156) 2172 (200) 0.373 Birth length, mean (SD), cm 46.6 (1.0) 44.8 (1.1) < 0.001*** Birth head circumference, mean (SD), cm 32.6 (1.1) 32.0 (1.3) 0.266 Twin, n (%) 3 (27) 0 (0) 0.245 Apgar score 1 min, median (IQR) 7 (6–8) 8 (6–8) 0.425 Apgar score 5 min, median (IQR) 8 (8–9) 9 (8–9) 0.285 Umbilical artery pH, mean (SD) 7.27 (0.06) 7.29 (0.07) 0.600 Marginal or membranous umbilical cord insertion, n (%) 1 (9) 3 (43) 0.245 Characteristics of mothers Maternal age, mean (SD), years old 34 (5) 35 (5) 0.697 Maternal weight at delivery, mean (SD), kg 62.7 (4.6) 61.7 (19.1) 0.905 Pre-pregnancy weight, mean (SD), kg 54.9 (6.3) 57.0 (17.9) 0.778 Maternal height, mean (SD), cm 156 (5) 159 (5) 0.146 Pre-pregnancy BMI, median (IQR) 22.4 (20.0-24.5) 21.1 (18.0-30.9) 0.475 Gestational weight gain, mean (SD), kg 7.9 (5.4) 4.8 (2.7) 0.134 Maternal alcohol consumption, n (%) 0 (0) 0 (0) Maternal smoking, n (%) 0 (0) 0 (0) Nulliparity, n (%) 11 (100) 3 (43) 0.011* Caesarean delivery, n (%) 10 (91) 5 (71) 0.528 PROM, n (%) 1 (9) 1 (14) 1.000 Gestational hypertension, n (%) 3 (27) 1 (14) 1.000 Diabetes mellitus, n (%) 2 (18) 0 (0) 0.497 SGA, small for gestational age; PROM, premature rupture of membrane; Diabetes mellitus, gestational and pre-gestational diabetes mellitus; SD, standard deviation; IQR, interquartile range. Identification of differential metabolites and pathway analysis in SGA infants To investigate metabolic alterations associated with intrauterine growth restriction, we analyzed umbilical cord serum samples using gas chromatography–tandem mass spectrometry (GC-MS/MS). A total of 242 metabolites were detected in all samples. For the multivariate analysis, we applied an orthogonal partial least-squares discriminant analysis (OPLS-DA) model. The score plot showed a clear separation between the SGA and control groups, indicating distinct clustering and substantial differences in the metabolic profiles (Fig. 3 A). To identify the specific metabolites responsible for this separation, we performed an S-plot analysis and assessed the variable importance in projection (VIP) scores. Six metabolites (galactose, glucose, allose, mannose, alanine, and lactic acid) were significantly altered in the SGA group in comparison to controls (Supplementary Table 2). A metabolic pathway enrichment analysis using MetaboAnalyst 6.0 revealed that these changes were associated with galactose metabolism, lactose degradation, and the glucose–alanine cycle (Fig. 3 B–C). These findings suggest that the glucose metabolism and energy homeostasis pathways are markedly affected in SGA infants. Discussion In this study, we conducted a comprehensive analysis of serum metabolites in the umbilical cord blood of term-born infants. We demonstrated that SGA infants exhibited significantly lower concentrations of metabolites involved in galactose metabolism, lactose degradation, and glucose-alanine cycle, all of which are essential for carbohydrate metabolism and energy homeostasis. Furthermore, infants in the SGA-short group exhibited significantly lower glutamine levels than those in the SGA-tall group. Previous studies have shown global downregulation of nutrient-related metabolic pathways, including galactose metabolism, in the cord blood and placenta of SGA infants, supporting the notion that intrauterine metabolic derangements play a key role in the pathogenesis of fetal growth restriction (FGR) 3 , 5 . Although several studies have reported altered glutamine levels in SGA infants, the direction of change remains inconsistent, with some studies describing elevated glutamine concentrations and others reporting reductions 6 – 8 . Our findings may help reconcile these discrepancies by highlighting the heterogeneity of metabolic phenotypes among SGA infants, particularly in relation to birth-body proportionality. Glutamine is one of the most abundant and metabolically active non-essential amino acids during fetal life, supporting diverse physiological processes including energy production, nucleotide and protein synthesis, cell proliferation, and redox regulation 9 , 10 . Among all amino acids, glutamine has one of the highest fetal-to-maternal plasma concentration ratios and is efficiently transported across the placenta 11 – 13 . In addition, a tightly regulated metabolic cycle exists in which fetal glutamate is taken up by the placenta, converted to glutamine, and then re-released into fetal circulation, thereby contributing to nitrogen and carbon homeostasis 14 . These observations underscore the central role of glutamine as a key metabolic substrate in fetal development. In the present study, glutamine concentrations were particularly reduced in SGA infants with impaired linear growth who also exhibited global downregulation of carbohydrate metabolism. Placental dysfunction in FGR is associated with the impaired function of multiple nutrient transporters, including glucose and glutamine transport systems 15 , 16 . Among these nutrients, glutamine has been shown to play a regulatory role in chondrocytes by supplying acetyl-CoA via glutamate dehydrogenase, which is required for histone acetylation and the expression of cartilage-specific genes 17 . Impaired glutamine metabolism in growth plate cartilage disrupts cellular proliferation and extracellular matrix synthesis, ultimately attenuating long bone elongation 17 . Our findings suggest that additional glutamine deficiency may act synergistically to unmask vulnerability to longitudinal growth in the context of nutritional restriction in SGA infants. Animal studies have shown that maternal glutamine supplementation improves fetal growth and reduces preweaning mortality in piglets 18 . These findings suggest that targeting glutamine and amino acid metabolism may represent a novel interventional strategy for SGA infants with linear growth restriction. In the present cohort, the SGA-short group had a higher proportion of primiparous mothers than the SGA-tall group. It is well known that primiparity is associated with less well-developed uteroplacental vascular remodeling, which may contribute to suboptimal nutrient delivery to the fetus 19 , 20 . While the SGA group in this study included a substantial number of multiparous mothers with hypertensive disorders of pregnancy, infants with impaired linear growth were more likely to be primiparous mothers. These findings suggest that SGA is not a homogeneous condition, and that maternal background may influence the phenotypic expression and underlying pathophysiology of fetal growth restriction. Several limitations of the present study warrant mention. First, the sample size was relatively small, particularly for the SGA-short group, which may have limited the statistical power. Second, the study was conducted at a single center using a retrospective design and the metabolomic analysis was limited to a subset of stored cord serum samples. Third, metabolite measurements were only performed at birth and the effects of postnatal nutrition were not assessed. Although our findings provide a novel perspective on the metabolic underpinnings of impaired linear growth in SGA infants, causal relationships between glutamine metabolism and postnatal outcomes cannot be determined from this study alone. Future longitudinal and interventional studies are needed to clarify the relationship between neonatal metabolic signatures and later growth and health trajectories. In conclusion, we identified altered glutamine metabolism in the umbilical cord serum of term SGA infants, particularly in those with both low weight and short birth length. These findings suggest that such metabolic profiles may not only reflect intrauterine nutritional status, but could also serve as early biomarkers of longitudinal growth potential or future metabolic risk. Further large-scale and longitudinal studies are warranted to determine whether birth metabolomic profiles can be used to predict postnatal outcomes and guide individualized strategies for the management of SGA infants. Methods Ethics This study was approved by the Ethics Committee of the Oita University Faculty of Medicine of Oita University Hospital, Japan (Oita University, Approval No. 2181, September 2021) and conducted in accordance with the principles of the Declaration of Helsinki. As this was a retrospective study, the Ethics Committee of Oita University Hospital waived the requirement for informed consent from infants born between April 2020 and September 2021, as they had already been discharged at the time of the study. However, these patients were given the opportunity to opt out of the study. Written informed consent was obtained from the parents or guardians of the infants born between October 2021 and July 2024. This study was conducted in compliance with the "Ethical Guidelines for Medical and Health Research Involving Human Subjects" established by the Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labour, and Welfare of Japan. Study population This study included term infants born at or after 37 weeks of gestation at Oita University Hospital between April 2020 and July 2024 who were managed in the NICU or GCU. Cases involving infants with multiple congenital anomalies, chromosomal abnormalities, severe anemia due to fetomaternal transfusion syndrome, or lack of parental consent were excluded. We focused on term infants because the umbilical cord blood metabolites analyzed in this study are known to be influenced by tocolytic agents used for threatened preterm labor, as previously reported 4 . Therefore, preterm infants were excluded from this study. At Oita University Hospital, infants born at or after 37 weeks of gestation are typically managed by obstetricians if they are asymptomatic. However, those with respiratory distress, hypoglycemia, or fever, as well as those with a birth weight of < 2300 g or classified as SGA with birth weight and length below the 10th percentile, are at higher risk for complications such as respiratory distress, hypoglycemia, and jaundice. The infants were managed by pediatricians in the NICU or GCU and were included in this study. Clinical observations Comprehensive clinical data were retrospectively collected from the participants' medical records, including the following aspects: (a) neonatal perinatal characteristics: sex, gestational age, birth weight, birth length, head circumference, singleton or multiple births, 1-minute and 5-minute Apgar scores, umbilical artery pH, and umbilical cord insertion site (marginal or membranous attachment to the placenta). Z-scores for birth weight and length were calculated based on gestational age and sex using the standardized reference values for neonatal anthropometry by gestational age reported by the Japan Pediatric Society Committee of Neonatal Medicine ( https://www.jpeds.or.jp/modules/guidelines/index.php?content_id=21 ). (b) Maternal characteristics: age at delivery, pre-pregnancy and pre-delivery weight, length, history of alcohol consumption and smoking, parity (primiparous or multiparous), mode of delivery, obstetric complications (including hypertensive disorders of pregnancy and diabetes mellitus [including both gestational and pre-gestational diabetes mellitus]), and presence of complications, such as premature rupture of membranes during labor. Sampling specimens and storage Umbilical cord blood samples were collected during delivery using plastic syringes. Samples were centrifuged for 10 min at 1200 × g , serum was decanted, and serum samples were stored at -80°C until use. Metabolomic analyses The analysis of metabolites was performed by GC-MS/MS. A GC-MS/MS analysis was performed on a GCMS-TQ8040 system (Shimadzu Corporation, Kyoto, Japan) equipped with a DB-5 capillary column (inner diameter, 30 m × 0.25 mm; film thickness. 1 µm; Agilent, Santa Clara, CA, USA). Each 1-µm aliquot of the derivatized sample solution was automatically injected in splitless mode into a gas-liquid chromatography column using an auto-injector (AOC-20i; Shimadzu Corporation). During the GCMS-TQ8040 analysis, the injector temperature was maintained at 280°C and helium was used as the carrier gas at a constant flow rate of 39.0 cm/s. The GC column temperature was programmed to remain at 100°C for 4 min, then increase to 320°C at a rate of 10°C/min, and held at 320°C for an additional 11 min. The ionization voltage was set to 70 eV. Argon was used for the collision-induced dissociation. Metabolite detection was performed using the Smart Metabolite Database Ver. 3 software program (Shimadzu Corporation) using a method described in a previous study, with some modifications 41 . The 2-isopropylmalic acid contained in the extraction solution was used to evaluate the stability of the GC-MS/MS analysis system. Peak identification was performed automatically and then confirmed manually based on the specific precursor and product ions as well as the retention time using the method described in our previous study 4 . The integral metabolomics datasets were imported into the SIMCA version 13.0.3.0 software program (Umetrics, Umea, Sweden) for multivariate statistical analyses. OPLS-DA with Pareto scaling was used to visualize the differences between the metabolomic datasets and extract the significant metabolites. The primary distinctions in metabolites between each group were identified through an S-plot analysis, which visualizes both the covariance and correlation between metabolites and the modelled class designation. Significant metabolites were selected based on compounds with p(corr) values > 0.6 and VIP values > 1.0, a metric that is commonly used to summarize the significance of each variable in model construction 43 . In the pathway analysis, to determine the pathways altered between metabolomics datasets, MetPA and MSEA with significant metabolites were performed using the MetaboAnalyst 6.0 software program ( https://www.metaboanalyst.ca/ , accessed on January 17, 2025), which is a free web-based tool that combines results from a potent pathway enrichment analysis pertaining to the conditions under study 21 . Statistical analyses Statistical analyses were performed using SPSS (ver. 29.0, IBM Corporation, Armonk, NY, USA) and GraphPad Prism (ver. 8, GraphPad Software, Inc., San Diego, CA, USA). The Shapiro–Wilk and Brown–Forsythe tests were used to assess the normality and homogeneity of variance, respectively. For comparisons between two groups, the Mann–Whitney U test was used for non-normally distributed data, with results presented as the median and interquartile ranges. The unpaired t-test was performed for normally distributed data. Statistical significance was set at p < 0.05. Declarations Data availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Acknowledgements The authors express their appreciation to the patients and their parents for their assistance in this study. We would also like to thank the Department of Pediatrics, Oita University Hospital, for their help with patient recruitment and sample collection and Kai S. for their excellent technical assistance. Mr. Brian Quinn of Japan Medical Communication for his assistance in editing this paper. Funding This work was supported by the Japan Society for the Promotion of Science (grant number 21K07774). Author information Authors and Affiliations Department of Pediatrics, Oita University Faculty of Medicine, Yufu, Oita, 879-5593, Japan Masanori Inoue, Kazuhito Sekiguchi, Shintaro Kishimoto, Tomoki Maeda, Kenji Ihara. Contributions M.I. and K.S. designed and proposed this study. M.I. and K.S. collected the clinical data. K.S. collected the samples and identified their metabolites. M.I., S.K., and T.M. analyzed and interpreted the data. M.I. drafted the manuscript. K.I. reviewed and edited the manuscript. All authors read, revised, and approved the final draft of the manuscript. Corresponding author Correspondence to Masanori Inoue. Ethics declarations Competing interests The authors declare no competing interests. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third-party material in this article are included in the article’s Creative Commons license unless otherwise indicated in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulations or exceeds the permitted use, permission is obtained directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. References Hokken-Koelega, A. C. S. et al. International Consensus Guideline on Small for Gestational Age: Etiology and Management From Infancy to Early Adulthood. Endocr. Rev. 44 , 539–565. 10.1210/endrev/bnad002 (2023). Clayton, P. E. et al. Management of the child born small for gestational age through to adulthood: a consensus statement of the International Societies of Pediatric Endocrinology and the Growth Hormone Research Society. J. Clin. Endocrinol. Metab. 92 , 804–810. 10.1210/jc.2006-2017 (2007). 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Supplementary Files SupplementaryTable.xlsx Cite Share Download PDF Status: Published Journal Publication published 28 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviews received at journal 16 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 06 May, 2025 Editor assigned by journal 06 May, 2025 Editor invited by journal 27 Apr, 2025 Submission checks completed at journal 25 Apr, 2025 First submitted to journal 25 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6525025","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":453268730,"identity":"0bfd8867-e6f6-429a-bf81-a4ff95bdb797","order_by":0,"name":"Masanori Inoue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYFACxgYQKcfAjBBKIEqLMQMzQg8hLRCQ2IBsDV6g28Dc9uBjm016Pzv/wQ+MbQzy/A0Mzx7g02J2gLHdcGZbWu7MZmZmCaAWwxkHGNIN8Gq5/7BNmrftcO6Gw8wM0n/bGBg3MDCkSRCwBaTlf7rBYWbmH0Bb7InVciABqIUN5LBEorRIzjiXbAj0i5kFwzmJ5BmHCfnlAPsziQ9ldvL8/Acf32Aos7Htb+9Je4BPCzoAOomZJ40UHWDAfoxkLaNgFIyCUTCsAQAK6T5tDZ0VRAAAAABJRU5ErkJggg==","orcid":"","institution":"Oita University","correspondingAuthor":true,"prefix":"","firstName":"Masanori","middleName":"","lastName":"Inoue","suffix":""},{"id":453268731,"identity":"e8b554e8-7d33-4ff2-afb3-1a9c1e6769d0","order_by":1,"name":"Kazuhito Sekiguchi","email":"","orcid":"","institution":"Oita University","correspondingAuthor":false,"prefix":"","firstName":"Kazuhito","middleName":"","lastName":"Sekiguchi","suffix":""},{"id":453268732,"identity":"a2c67b2d-d8a9-4a0b-a8a1-26dd0b281c02","order_by":2,"name":"Shintaro Kishimoto","email":"","orcid":"","institution":"Oita University","correspondingAuthor":false,"prefix":"","firstName":"Shintaro","middleName":"","lastName":"Kishimoto","suffix":""},{"id":453268733,"identity":"d0c333a9-b28f-411a-b755-37d5f437aed6","order_by":3,"name":"Tomoki Maeda","email":"","orcid":"","institution":"Oita University","correspondingAuthor":false,"prefix":"","firstName":"Tomoki","middleName":"","lastName":"Maeda","suffix":""},{"id":453268734,"identity":"c0fe1c50-248b-495c-967d-707fe549e32f","order_by":4,"name":"Kenji Ihara","email":"","orcid":"","institution":"Oita University","correspondingAuthor":false,"prefix":"","firstName":"Kenji","middleName":"","lastName":"Ihara","suffix":""}],"badges":[],"createdAt":"2025-04-25 04:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6525025/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6525025/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-12856-0","type":"published","date":"2025-07-28T16:21:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82581959,"identity":"e32d19a8-3288-40ec-9888-6d3f39a2f109","added_by":"auto","created_at":"2025-05-13 06:40:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":33584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the study of small for gestational age (SGA) infants.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-6525025/v1/58fd8e38506fe4946727d74e.png"},{"id":82581960,"identity":"067c5fe1-41f2-4bb3-850c-c0015b9af76e","added_by":"auto","created_at":"2025-05-13 06:40:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":33275,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between birth weight and length Z scores.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) A linear regression analysis of birth weight and length Z-scores in all term infants. (B-D) A subgroup analysis of birth weight and length Z-scores in appropriate for gestational age (B), large for gestational age (C), and small for gestational age (D) infants.\u003c/p\u003e","description":"","filename":"Figure22.png","url":"https://assets-eu.researchsquare.com/files/rs-6525025/v1/e3950425710bfdc1efb291a1.png"},{"id":82581965,"identity":"3e22aad4-260e-48ae-b32b-de508c42b391","added_by":"auto","created_at":"2025-05-13 06:40:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":158722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabolomic differences between the SGA and control groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Score plot of an orthogonal partial least-squares discriminant analysis comparing metabolomic profiles between the small for gestational age (SGA) and control groups. (B-C) A pathway analysis identified galactose metabolism, lactose degradation, and the glucose-alanine cycle as key pathways altered in the SGA group, suggesting that intrauterine growth restriction is associated with metabolic changes related to glucose and energy homeostasis.\u003c/p\u003e","description":"","filename":"Figure23.png","url":"https://assets-eu.researchsquare.com/files/rs-6525025/v1/f6c299e815dc73e428699d25.png"},{"id":82581961,"identity":"d4595e4c-4e0a-4631-8674-0b9de848a9e1","added_by":"auto","created_at":"2025-05-13 06:40:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68920,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistinct metabolic profiles between the SGA-tall and SGA-short groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Score plot of the orthogonal partial least-squares discriminant analysis comparing metabolomic profiles between the SGA-tall and SGA-shortgroups. (B) Glutamine in serum of umbilical cord blood was identified as a significantly different metabolite between the SGA-tall and SGA-short groups. Error bars indicate the standard deviation. *p \u0026lt; 0.05. The statistical analysis and figure generation were performed using GraphPad (ver. 8).\u003c/p\u003e","description":"","filename":"Figure24.png","url":"https://assets-eu.researchsquare.com/files/rs-6525025/v1/fb3a0bacb43e1174ab493675.png"},{"id":88268210,"identity":"5326ef69-ceb1-4888-9a79-f72702927bcb","added_by":"auto","created_at":"2025-08-04 16:50:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1244038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6525025/v1/f828a1e8-ccca-4723-aa54-7a8b096ea185.pdf"},{"id":82581962,"identity":"4818d16a-ee00-4d83-a159-30708395dc48","added_by":"auto","created_at":"2025-05-13 06:40:04","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16417,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6525025/v1/f59458001915bc7e0761c58f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metabolomic analysis of umbilical cord serum in small-for-gestational-age infants with a focus on linear growth","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSmall-for-gestational-age (SGA) infants, defined as those with a birth weight below the 10th percentile for gestational age, are known to be at increased risk for a range of short- and long-term health complications, including metabolic syndrome, insulin resistance, and impaired growth trajectories \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Considerable heterogeneity exists among SGA infants in terms of postnatal growth patterns and metabolic outcomes, which suggests underlying differences in intrauterine growth pathways and biological profiles \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent advances in metabolomics have enabled comprehensive analyses of metabolic profiles, providing novel insights into fetal metabolic adaptations to intrauterine growth restriction. Studies utilizing umbilical cord blood, the placenta, and maternal serum have begun to elucidate these adaptive mechanisms \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Several investigations, including those involving term-born infants, have reported metabolomic signatures associated with SGA \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, few studies have stratified SGA infants based on birth body proportionality, such as birth length Z scores or ponderal index, despite evidence suggesting that these anthropometric characteristics may reflect distinct etiologies and differing risks of persistent postnatal growth failure.\u003c/p\u003e \u003cp\u003eIn this study, we conducted a comprehensive metabolomic analysis of nutritional metabolites in umbilical cord serum samples from term infants using gas chromatography\u0026ndash;mass spectrometry. By subclassifying SGA infants based on birth length Z-scores, we aimed to identify metabolic features associated with disproportionate growth patterns and to explore potential biomarkers that may reflect the intrauterine nutritional status or predict future growth trajectories.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePerinatal characteristics\u003c/h2\u003e \u003cp\u003eTo identify the factors involved in the growth of SGA infants, we investigated the perinatal characteristics and nutritional metabolite profiles of umbilical cord serum. Since preterm infants often receive treatment for threatened preterm labor immediately before birth, and such treatments are known to acutely affect fetal metabolism and alter the profile of cord blood metabolites \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, we limited our analysis to term infants born at or after 37 weeks of gestation.\u003c/p\u003e \u003cp\u003eDuring the study period, 462 neonates were admitted to the neonatal intensive care unit (NICU) or growing care unit (GCU) of our hospital, of whom 267 were born at term (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Residual cord blood samples obtained during routine clinical care were available for 81 infants. After excluding 15 cases due to multiple congenital anomalies or chromosomal abnormalities (n\u0026thinsp;=\u0026thinsp;5), severe anemia due to twin-to-twin transfusion syndrome (n\u0026thinsp;=\u0026thinsp;1), or lack of parental consent (n\u0026thinsp;=\u0026thinsp;9), 66 infants were included in the final analysis. Among them, 48 infants with birth weights above the 10th percentile were designated as the control group, and 18 infants with birth weights below the 10th percentile were classified as the SGA group. As our institution serves as a regional perinatal referral center, we receive many high-risk pregnancies from local obstetric clinics, including those complicated by fetal growth restriction and maternal comorbidities. Consequently, the proportion of SGA infants in our cohort was higher than that of the general population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe main reason for admission in both the control and SGA groups was transient tachypnea of the newborn, accounting for approximately 70% of the cases in both groups (Supplementary Table\u0026nbsp;1). According to our institutional protocol, infants with birth weight\u0026thinsp;\u0026lt;\u0026thinsp;2300 g or with both weight and length below the 10th percentile are admitted to the NICU or GCU for close observation and management of respiratory status, glucose levels, and infection risk. These criteria accounted for 89% of the admissions in the SGA group. Other reasons for admission included transient neonatal hypoglycemia, suspected infection, and meconium aspiration syndrome.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the perinatal characteristics of the SGA infants. As expected, birth weight, length, and head circumference were significantly lower in the SGA group than in the control group. No significant differences were observed between the two groups in terms of sex, gestational age, rate of twin pregnancy, Apgar scores at 1 and 5 min, umbilical artery pH, or the incidence of marginal or membranous umbilical cord insertion. Regarding maternal characteristics, the SGA group had a significantly higher proportion of multiparous women and a higher incidence of hypertensive disorders of pregnancy than the control group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerinatal characteristics of term infants in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;48\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSGA group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of infants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age, median (IQR), weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.1 (37.4\u0026ndash;39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.8 (37.3\u0026ndash;38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight, median (IQR), g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2963 (2651\u0026ndash;3530)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2268 (2087\u0026ndash;2344)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth length, mean (SD), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.2 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.9 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth head circumference, mean (SD), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.2 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.4 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score 1 min, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (6\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.692\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score 5 min, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUmbilical artery pH, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.28 (7.26\u0026ndash;7.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.29 (7.22\u0026ndash;7.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarginal or membranous umbilical cord insertion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of mothers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age, mean (SD), years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal weight at delivery, mean (SD), kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.7 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.3 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy weight, median (IQR), kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.5 (46.8\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.5 (48.5\u0026ndash;62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal height, median (IQR), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158 (154\u0026ndash;163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.1 (153\u0026ndash;162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.6 (19.5\u0026ndash;27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.4 (19.5\u0026ndash;27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational weight gain, mean (SD), kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.3 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal alcohol consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNulliparity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.042*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaesarean delivery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational hypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePROM, premature rupture of membranes; Diabetes mellitus, gestational and pre-gestational diabetes mellitus; SD, standard deviation; IQR, interquartile range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation between weight and length Z-scores in SGA infants\u003c/h3\u003e\n\u003cp\u003eTo examine the relationship between birth weight and birth length in our cohort, we performed a linear regression analysis using Z-scores derived from standardized reference data. Across the entire term population, a significant positive correlation was observed between birth weight and length Z-scores (R\u0026sup2; = 0.64, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). We then conducted subgroup analyses based on the following birth weight categories: appropriate for gestational age (AGA; 10th\u0026ndash;90th percentile), large for gestational age (LGA; above the 90th percentile), and SGA (below the 10th percentile). A moderate positive correlation was observed in the AGA group (R\u0026sup2; = 0.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). No significant correlation was found in either the LGA or SGA groups (R\u0026sup2; = 0.21 and 0.064, respectively; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Although both the LGA and SGA groups lacked statistically significant correlations, the R\u0026sup2; value was lower in the SGA group (0.21 vs. 0.064), indicating a more pronounced dissociation between weight and length in these infants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis observation led us to hypothesize that the SGA group may include two distinct subgroups: one with relatively preserved birth length despite low birth weight and another with reductions in both parameters. To explore this possibility, we subdivided the SGA group based on birth length Z-scores using \u0026minus;\u0026thinsp;1.28 (corresponding to the 10th percentile) as the cutoff value. Infants with a birth length Z score above \u0026minus;\u0026thinsp;1.28 were categorized as the SGA-tall group (n\u0026thinsp;=\u0026thinsp;11), and those below \u0026minus;\u0026thinsp;1.28 were categorized as the SGA-short group (n\u0026thinsp;=\u0026thinsp;7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant differences were observed between these subgroups in terms of sex, gestational age, birth weight, head circumference, rate of twin pregnancy, Apgar scores, umbilical artery pH, or frequency of marginal or membranous cord insertion. However, a higher proportion of primiparous mothers were observed in the SGA-short group than in the SGA-tall group (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePerinatal characteristics of small for gestational age infants classified by short stature status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGA-tall group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSGA-short group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of infants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age, mean (SD), weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38.1 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.4 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight, mean (SD), g\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2249 (156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2172 (200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth length, mean (SD), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.6 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.8 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth head circumference, mean (SD), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.6 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.0 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTwin, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score 1 min, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (6\u0026ndash;8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApgar score 5 min, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (8\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUmbilical artery pH, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.27 (0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.29 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarginal or membranous umbilical cord insertion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCharacteristics of mothers\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal age, mean (SD), years old\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal weight at delivery, mean (SD), kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.7 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.7 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy weight, mean (SD), kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.9 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.0 (17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal height, mean (SD), cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-pregnancy BMI, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.4 (20.0-24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1 (18.0-30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational weight gain, mean (SD), kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.9 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.8 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal alcohol consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaternal smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNulliparity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaesarean delivery, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePROM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational hypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSGA, small for gestational age; PROM, premature rupture of membrane; Diabetes mellitus, gestational and pre-gestational diabetes mellitus; SD, standard deviation; IQR, interquartile range.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eIdentification of differential metabolites and pathway analysis in SGA infants\u003c/h3\u003e\n\u003cp\u003eTo investigate metabolic alterations associated with intrauterine growth restriction, we analyzed umbilical cord serum samples using gas chromatography\u0026ndash;tandem mass spectrometry (GC-MS/MS). A total of 242 metabolites were detected in all samples. For the multivariate analysis, we applied an orthogonal partial least-squares discriminant analysis (OPLS-DA) model. The score plot showed a clear separation between the SGA and control groups, indicating distinct clustering and substantial differences in the metabolic profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo identify the specific metabolites responsible for this separation, we performed an S-plot analysis and assessed the variable importance in projection (VIP) scores. Six metabolites (galactose, glucose, allose, mannose, alanine, and lactic acid) were significantly altered in the SGA group in comparison to controls (Supplementary Table\u0026nbsp;2). A metabolic pathway enrichment analysis using MetaboAnalyst 6.0 revealed that these changes were associated with galactose metabolism, lactose degradation, and the glucose\u0026ndash;alanine cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u0026ndash;C). These findings suggest that the glucose metabolism and energy homeostasis pathways are markedly affected in SGA infants.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we conducted a comprehensive analysis of serum metabolites in the umbilical cord blood of term-born infants. We demonstrated that SGA infants exhibited significantly lower concentrations of metabolites involved in galactose metabolism, lactose degradation, and glucose-alanine cycle, all of which are essential for carbohydrate metabolism and energy homeostasis. Furthermore, infants in the SGA-short group exhibited significantly lower glutamine levels than those in the SGA-tall group. Previous studies have shown global downregulation of nutrient-related metabolic pathways, including galactose metabolism, in the cord blood and placenta of SGA infants, supporting the notion that intrauterine metabolic derangements play a key role in the pathogenesis of fetal growth restriction (FGR) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Although several studies have reported altered glutamine levels in SGA infants, the direction of change remains inconsistent, with some studies describing elevated glutamine concentrations and others reporting reductions \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Our findings may help reconcile these discrepancies by highlighting the heterogeneity of metabolic phenotypes among SGA infants, particularly in relation to birth-body proportionality.\u003c/p\u003e \u003cp\u003eGlutamine is one of the most abundant and metabolically active non-essential amino acids during fetal life, supporting diverse physiological processes including energy production, nucleotide and protein synthesis, cell proliferation, and redox regulation \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Among all amino acids, glutamine has one of the highest fetal-to-maternal plasma concentration ratios and is efficiently transported across the placenta \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In addition, a tightly regulated metabolic cycle exists in which fetal glutamate is taken up by the placenta, converted to glutamine, and then re-released into fetal circulation, thereby contributing to nitrogen and carbon homeostasis \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. These observations underscore the central role of glutamine as a key metabolic substrate in fetal development. In the present study, glutamine concentrations were particularly reduced in SGA infants with impaired linear growth who also exhibited global downregulation of carbohydrate metabolism. Placental dysfunction in FGR is associated with the impaired function of multiple nutrient transporters, including glucose and glutamine transport systems \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Among these nutrients, glutamine has been shown to play a regulatory role in chondrocytes by supplying acetyl-CoA via glutamate dehydrogenase, which is required for histone acetylation and the expression of cartilage-specific genes \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Impaired glutamine metabolism in growth plate cartilage disrupts cellular proliferation and extracellular matrix synthesis, ultimately attenuating long bone elongation \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Our findings suggest that additional glutamine deficiency may act synergistically to unmask vulnerability to longitudinal growth in the context of nutritional restriction in SGA infants. Animal studies have shown that maternal glutamine supplementation improves fetal growth and reduces preweaning mortality in piglets \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. These findings suggest that targeting glutamine and amino acid metabolism may represent a novel interventional strategy for SGA infants with linear growth restriction.\u003c/p\u003e \u003cp\u003eIn the present cohort, the SGA-short group had a higher proportion of primiparous mothers than the SGA-tall group. It is well known that primiparity is associated with less well-developed uteroplacental vascular remodeling, which may contribute to suboptimal nutrient delivery to the fetus \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. While the SGA group in this study included a substantial number of multiparous mothers with hypertensive disorders of pregnancy, infants with impaired linear growth were more likely to be primiparous mothers. These findings suggest that SGA is not a homogeneous condition, and that maternal background may influence the phenotypic expression and underlying pathophysiology of fetal growth restriction.\u003c/p\u003e \u003cp\u003eSeveral limitations of the present study warrant mention. First, the sample size was relatively small, particularly for the SGA-short group, which may have limited the statistical power. Second, the study was conducted at a single center using a retrospective design and the metabolomic analysis was limited to a subset of stored cord serum samples. Third, metabolite measurements were only performed at birth and the effects of postnatal nutrition were not assessed. Although our findings provide a novel perspective on the metabolic underpinnings of impaired linear growth in SGA infants, causal relationships between glutamine metabolism and postnatal outcomes cannot be determined from this study alone. Future longitudinal and interventional studies are needed to clarify the relationship between neonatal metabolic signatures and later growth and health trajectories.\u003c/p\u003e \u003cp\u003eIn conclusion, we identified altered glutamine metabolism in the umbilical cord serum of term SGA infants, particularly in those with both low weight and short birth length. These findings suggest that such metabolic profiles may not only reflect intrauterine nutritional status, but could also serve as early biomarkers of longitudinal growth potential or future metabolic risk. Further large-scale and longitudinal studies are warranted to determine whether birth metabolomic profiles can be used to predict postnatal outcomes and guide individualized strategies for the management of SGA infants.\u003c/p\u003e "},{"header":"Methods","content":"\u003ch2\u003eEthics\u003c/h2\u003e\u003cp\u003e This study was approved by the Ethics Committee of the Oita University Faculty of Medicine of Oita University Hospital, Japan (Oita University, Approval No. 2181, September 2021) and conducted in accordance with the principles of the Declaration of Helsinki. As this was a retrospective study, the Ethics Committee of Oita University Hospital waived the requirement for informed consent from infants born between April 2020 and September 2021, as they had already been discharged at the time of the study. However, these patients were given the opportunity to opt out of the study. Written informed consent was obtained from the parents or guardians of the infants born between October 2021 and July 2024. This study was conducted in compliance with the \"Ethical Guidelines for Medical and Health Research Involving Human Subjects\" established by the Ministry of Education, Culture, Sports, Science and Technology and the Ministry of Health, Labour, and Welfare of Japan.\u003c/p\u003e\u003ch3\u003eStudy population\u003c/h3\u003e\u003cp\u003eThis study included term infants born at or after 37 weeks of gestation at Oita University Hospital between April 2020 and July 2024 who were managed in the NICU or GCU. Cases involving infants with multiple congenital anomalies, chromosomal abnormalities, severe anemia due to fetomaternal transfusion syndrome, or lack of parental consent were excluded. We focused on term infants because the umbilical cord blood metabolites analyzed in this study are known to be influenced by tocolytic agents used for threatened preterm labor, as previously reported\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Therefore, preterm infants were excluded from this study. At Oita University Hospital, infants born at or after 37 weeks of gestation are typically managed by obstetricians if they are asymptomatic. However, those with respiratory distress, hypoglycemia, or fever, as well as those with a birth weight of \u0026lt; 2300 g or classified as SGA with birth weight and length below the 10th percentile, are at higher risk for complications such as respiratory distress, hypoglycemia, and jaundice. The infants were managed by pediatricians in the NICU or GCU and were included in this study.\u003c/p\u003e\u003ch2\u003eClinical observations\u003c/h2\u003e\u003cp\u003eComprehensive clinical data were retrospectively collected from the participants' medical records, including the following aspects: (a) neonatal perinatal characteristics: sex, gestational age, birth weight, birth length, head circumference, singleton or multiple births, 1-minute and 5-minute Apgar scores, umbilical artery pH, and umbilical cord insertion site (marginal or membranous attachment to the placenta). Z-scores for birth weight and length were calculated based on gestational age and sex using the standardized reference values for neonatal anthropometry by gestational age reported by the Japan Pediatric Society Committee of Neonatal Medicine (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jpeds.or.jp/modules/guidelines/index.php?content_id=21\u003c/span\u003e\u003cspan address=\"https://www.jpeds.or.jp/modules/guidelines/index.php?content_id=21\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). (b) Maternal characteristics: age at delivery, pre-pregnancy and pre-delivery weight, length, history of alcohol consumption and smoking, parity (primiparous or multiparous), mode of delivery, obstetric complications (including hypertensive disorders of pregnancy and diabetes mellitus [including both gestational and pre-gestational diabetes mellitus]), and presence of complications, such as premature rupture of membranes during labor.\u003c/p\u003e\u003ch2\u003eSampling specimens and storage\u003c/h2\u003e\u003cp\u003eUmbilical cord blood samples were collected during delivery using plastic syringes. Samples were centrifuged for 10 min at 1200 × \u003cem\u003eg\u003c/em\u003e, serum was decanted, and serum samples were stored at -80°C until use.\u003c/p\u003e\u003ch2\u003eMetabolomic analyses\u003c/h2\u003e\u003cp\u003eThe analysis of metabolites was performed by GC-MS/MS. A GC-MS/MS analysis was performed on a GCMS-TQ8040 system (Shimadzu Corporation, Kyoto, Japan) equipped with a DB-5 capillary column (inner diameter, 30 m × 0.25 mm; film thickness. 1 µm; Agilent, Santa Clara, CA, USA). Each 1-µm aliquot of the derivatized sample solution was automatically injected in splitless mode into a gas-liquid chromatography column using an auto-injector (AOC-20i; Shimadzu Corporation). During the GCMS-TQ8040 analysis, the injector temperature was maintained at 280°C and helium was used as the carrier gas at a constant flow rate of 39.0 cm/s. The GC column temperature was programmed to remain at 100°C for 4 min, then increase to 320°C at a rate of 10°C/min, and held at 320°C for an additional 11 min. The ionization voltage was set to 70 eV. Argon was used for the collision-induced dissociation. Metabolite detection was performed using the Smart Metabolite Database Ver. 3 software program (Shimadzu Corporation) using a method described in a previous study, with some modifications\u003csup\u003e41\u003c/sup\u003e. The 2-isopropylmalic acid contained in the extraction solution was used to evaluate the stability of the GC-MS/MS analysis system. Peak identification was performed automatically and then confirmed manually based on the specific precursor and product ions as well as the retention time using the method described in our previous study\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe integral metabolomics datasets were imported into the SIMCA version 13.0.3.0 software program (Umetrics, Umea, Sweden) for multivariate statistical analyses. OPLS-DA with Pareto scaling was used to visualize the differences between the metabolomic datasets and extract the significant metabolites. The primary distinctions in metabolites between each group were identified through an S-plot analysis, which visualizes both the covariance and correlation between metabolites and the modelled class designation. Significant metabolites were selected based on compounds with p(corr) values \u0026gt; 0.6 and VIP values \u0026gt; 1.0, a metric that is commonly used to summarize the significance of each variable in model construction\u003csup\u003e43\u003c/sup\u003e. In the pathway analysis, to determine the pathways altered between metabolomics datasets, MetPA and MSEA with significant metabolites were performed using the MetaboAnalyst 6.0 software program (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on January 17, 2025), which is a free web-based tool that combines results from a potent pathway enrichment analysis pertaining to the conditions under study\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eStatistical analyses\u003c/h2\u003e\u003cp\u003eStatistical analyses were performed using SPSS (ver. 29.0, IBM Corporation, Armonk, NY, USA) and GraphPad Prism (ver. 8, GraphPad Software, Inc., San Diego, CA, USA). The Shapiro–Wilk and Brown–Forsythe tests were used to assess the normality and homogeneity of variance, respectively. For comparisons between two groups, the Mann–Whitney U test was used for non-normally distributed data, with results presented as the median and interquartile ranges. The unpaired t-test was performed for normally distributed data. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their appreciation to the patients and their parents for their assistance in this study. We would also like to thank the Department of Pediatrics, Oita University Hospital, for their help with patient recruitment and sample collection and Kai S. for their excellent technical assistance. Mr. Brian Quinn of Japan Medical Communication for his assistance in editing this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (grant number 21K07774).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Pediatrics, Oita University Faculty of Medicine, Yufu, Oita, 879-5593, Japan\u003c/p\u003e\n\u003cp\u003eMasanori Inoue, Kazuhito Sekiguchi, Shintaro Kishimoto, Tomoki Maeda, Kenji Ihara.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.I. and K.S. designed and proposed this study. M.I. and K.S. collected the clinical data. K.S. collected the samples and identified their metabolites. M.I., S.K., and T.M. analyzed and interpreted the data. M.I. drafted the manuscript. K.I. reviewed and edited the manuscript. All authors read, revised, and approved the final draft of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Masanori Inoue.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRights and permissions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Access\u003c/strong\u003e This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third-party material in this article are included in the article\u0026rsquo;s Creative Commons license unless otherwise indicated in a credit line to the material. If material is not included in the article\u0026rsquo;s Creative Commons license and your intended use is not permitted by statutory regulations or exceeds the permitted use, permission is obtained directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHokken-Koelega, A. C. S. et al. International Consensus Guideline on Small for Gestational Age: Etiology and Management From Infancy to Early Adulthood. \u003cem\u003eEndocr. 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Protoc.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 1735\u0026ndash;1761. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41596-022-00710-w\u003c/span\u003e\u003cspan address=\"10.1038/s41596-022-00710-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cord blood, Glutamine, Infant, Metabolomics, Small for gestational age","lastPublishedDoi":"10.21203/rs.3.rs-6525025/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6525025/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmall-for-gestational-age (SGA) infants exhibit considerable heterogeneity in growth trajectories and metabolic outcomes; however, the metabolic basis underlying distinct phenotypes remains poorly understood. In this study, we analyzed umbilical cord serum samples (term infants, n\u0026thinsp;=\u0026thinsp;66; SGA infants, n\u0026thinsp;=\u0026thinsp;18; controls, n\u0026thinsp;=\u0026thinsp;48), using gas chromatography\u0026ndash;mass spectrometry to explore nutritional metabolic profiles. A metabolomic analysis revealed that SGA infants had significantly lower levels of metabolites involved in galactose metabolism, lactose degradation, and the glucose\u0026ndash;alanine cycle, indicating altered carbohydrate metabolism and energy homeostasis. Among SGA infants, those with both low weight and short length at birth (SGA-short) had significantly reduced glutamine concentrations in comparison to those with preserved length (SGA-tall). The SGA-short group also had a higher proportion of primiparous mothers. Glutamine is essential for fetal growth, particularly skeletal growth, and its deficiency may exacerbate linear growth impairment in the context of restricted intrauterine nutrition. These findings highlight the importance of metabolic subclassification in SGA infants and suggest that glutamine-related pathways could serve as potential biomarkers or therapeutic targets for infants at risk of postnatal growth failure.\u003c/p\u003e","manuscriptTitle":"Metabolomic analysis of umbilical cord serum in small-for-gestational-age infants with a focus on linear growth","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 06:40:00","doi":"10.21203/rs.3.rs-6525025/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-10T05:57:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-02T16:11:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-17T03:50:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"38589697122812189479109506519727983659","date":"2025-05-13T15:24:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335719860320630263289821810163322025917","date":"2025-05-09T03:25:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-06T14:19:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-06T14:18:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-28T03:12:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-25T05:37:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-25T04:06:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f832315e-df01-4562-81ea-d72114a9d7f9","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48202455,"name":"Health sciences/Medical research/Paediatric research"},{"id":48202456,"name":"Health sciences/Health care/Nutrition"}],"tags":[],"updatedAt":"2025-08-04T16:40:32+00:00","versionOfRecord":{"articleIdentity":"rs-6525025","link":"https://doi.org/10.1038/s41598-025-12856-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-28 16:21:01","publishedOnDateReadable":"July 28th, 2025"},"versionCreatedAt":"2025-05-13 06:40:00","video":"","vorDoi":"10.1038/s41598-025-12856-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-12856-0","workflowStages":[]},"version":"v1","identity":"rs-6525025","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6525025","identity":"rs-6525025","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}