Associations of Umbilical Cord Blood Amino Acid Profiles and Insulin-Like Growth Factor 1 With Birth Weight

Associations of Umbilical Cord Blood Amino Acid Profiles and Insulin-Like Growth Factor 1 With Birth Weight | 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 Associations of Umbilical Cord Blood Amino Acid Profiles and Insulin-Like Growth Factor 1 With Birth Weight Yuki Kawashima-Sonoyama, Mako Ago, Aisa Moriyama, Kei Yamamoto, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8446599/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective To investigate whether umbilical cord blood (UCB) amino acid profiles are affected by newborn birth weight (BW), insulin-like growth factor 1 (IGF-1), and maternal/placental factors. Study Design We conducted a prospective, single-center, observational study using UCBs from 16 light-for-date (LFD) and 61 appropriate-for-dates (AFD) infants. The 20 amino acids in UCBs were measured using liquid chromatography-tandem mass spectrometry. Random forest analysis identified factors influencing the amino acid (AA) profile and BW. Result BW was positively correlated with maternal body mass index and placental weight but not with IGF-1. LFD infants had higher levels of glycine, phenylalanine, methionine, and asparagine than AFD infants. Random forest analysis identified glycine, phenylalanine, asparagine, arginine, and lysine as the top contributors to LFD or AFD. Conclusion Although IGF-1 levels were similar, AA profiles differed from those of AFD infants, suggesting that profiling may identify LFD infants beyond IGF-1 levels. Health sciences/Health care/Paediatrics Health sciences/Biomarkers/Predictive markers Figures Figure 1 Figure 2 Figure 3 Introduction Low birth weight (LBW; BW < 2,500 g) is a significant global health concern because it increases the risk of neonatal mortality, impairs child growth and neurodevelopment, and increases the likelihood of developing chronic diseases in adulthood ( 1 ). In Japan, the total number of live births has steadily declined over the past few decades; however, the proportion of LBW infants has continued to increase ( 2 ). One of the major contributing factors to this phenomenon is the rising prevalence of undernutrition among pregnant women ( 3 ). Consequently, LBW infants face an increased risk of postnatal growth impairment and higher susceptibility to adult-onset metabolic disorders, including type 2 diabetes, hypertension, and chronic kidney disease ( 4 ). This concept, widely known as the Developmental Origins of Health and Disease (DOHaD), proposes that an individual’s future health and disease risks are strongly influenced by the intrauterine and early postnatal environments ( 5 ). Therefore, growing attention has been directed toward elucidating the mechanisms underlying LBW associated with maternal undernutrition, along with efforts to establish preventive and therapeutic approaches ( 6 ). Insulin-like growth factor 1 (IGF-1) is a crucial regulator of fetal and postnatal growth ( 7 ). While growth hormone primarily stimulates IGF-1 production, IGF-1 production is also closely linked to nutritional status ( 8 ). Previous studies have shown a positive correlation between BW and IGF-1 levels in umbilical cord blood (UCB) ( 9 ). Recently, we discovered that different amino acid (AA) profiles can influence IGF-1 secretion, indicating a potential regulatory mechanism between fetal nutrition and growth factor activity in rodents and hepatocyte-derived cell lines ( 10 ). However, the detailed relationships between specific nutrient profiles, such as AA composition and IGF-1 concentrations, have not yet been fully elucidated in human samples. UCB serves as a valuable window into the intrauterine nutritional environment, reflecting both maternal and placental influences on fetal development ( 11 ). AAs are essential nutrients for protein synthesis, growth, and maintenance of physiological functions. Essential and conditionally essential AAs must be obtained exclusively from the diet and are therefore considered indicators of nutritional status ( 12 ). Therefore, analyzing AA profiles in UCBs and their associations with IGF-1 levels, BW, and maternal parameters may offer novel insights into the nutritional mechanisms underlying fetal growth. In this study, we investigated AA profiles in UCB from newborns and examined their associations with BW, IGF-1 levels, and maternal factors. Additionally, to clarify the metabolic adaptations associated with fetal growth restriction, we performed a random forest analysis to identify key AAs that differentiate LBW infants from their normal-BW counterparts. Methods Study Design and Setting This prospective, single-center, observational study was conducted at the Department of Pediatrics, Shimane University Hospital, Japan. Participants The eligible participants were neonates born at Shimane University Hospital between 37 + 0 and 41 + 6 weeks of gestation, and their mothers. Inclusion was determined based on the availability of UCB immediately after birth, and written informed consent was obtained from the mothers. Stillbirths were excluded from the study. Infants classified as light-for-dates (LFD) were defined as those with BWs below the 10th percentile for gestational age (regardless of length), whereas appropriate-for-dates (AFD) infants had BWs above the 10th percentile. Among the various definitions for LBW infants, we used the LFD criterion to increase the number of eligible cases in this study. Sampling of UCBs and Analysis of AAs and IGF-1 UCB (2.5 mL) was collected from each neonate during delivery using a plastic syringe. Blood samples were processed to separate serum and plasma, centrifuged at 1,200 × g for 10 min. The supernatant was collected, and the serum was decanted; serum samples were stored at − 20°C until use. Plasma AA profiles were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Serum levels of growth-related hormones, including IGF-1, were also measured ( 10 ). Clinical and Demographic Data We obtained information for mothers and newborns during the perinatal period, including maternal characteristics (age, pregnancy weight and height, body mass index (BMI), complications, medication use, pre-delivery fasting time, and newborn data (gestational age, BW, birth length, head circumference, Apgar scores, mode of delivery, and placental weight). Details regarding the timing of the last meal and intravenous fluid administration during labor were also recorded. Statistical and Machine Learning Analyses The collected data, including AA profiles, IGF-1 levels, and clinical parameters, were subjected to statistical and random forest analyses to identify characteristic patterns associated with LBW. In the statistical analysis, the Pearson correlation coefficient (r) values and the associated t or P values were calculated using Excel (Microsoft Corporation) as follows: r value = CORREL (Array1, Array2), t value = |r * √(N – 2) / √(1 – r 2 )|, P value = TDIST (t, N − 2, 2). CORREL and TDIST are functions in Excel. Array1 and Array2 were composed of BW, IGF-1, amino acids, or maternal factors. N is the number of samples. Random forest analysis was performed using Python 3.12.11, scikit-learn 1.6.1, NumPy 2.0.2, and Pandas 2.2.2. Other dependencies followed the default Google Colaboratory runtime version at the time of the analysis. We trained scikit-learn’s random forest classifier using umbilical cord blood amino acid concentrations as predictors and two-class labels (AFD and LFD) as the binary outcomes. The models were fitted using the following settings: n_estimators = 10, max_depth = None, bootstrap = False, min_samples_leaf = 2, min_samples_split = 2, max_features = 'sqrt,’ and random_state = 0. We also described impurity-based (Gini) feature importance. Ethical Considerations This study was conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan. The study protocol was approved by the Medical Research Ethics Committee of the Shimane University Faculty of Medicine (Approval Number: KS20220904-1). Written informed consent was obtained from the guardians of all participants. All personal data were anonymized prior to analysis. Results Characteristics of study participants The characteristics of the study participants are summarized in Table 1 and Supplementary Table 1. A total of 16 and 61 infants were included in the LFD and AFD groups, respectively. The gestational age at birth was similar between the groups. As expected, BW, birth length, and their SD scores were significantly lower in the LFD group than in the AFD group (all P < 0.001). Maternal age and BMI before pregnancy and at delivery did not differ between the groups. Maternal gestational weight gain was significantly lower in the LFD group (8.3 ± 3.3 kg) than that in the AFD group (10.4 ± 2.1 kg, P = 0.026). The rates of cesarean delivery and painless labor were not significantly different, although painless labor tended to be more frequent in the LFD group ( P = 0.053). The interval between the last meal and delivery was comparable between the groups. Placental weight was significantly lower in the LFD group than in the AFD group (422.3 ± 84.4 g vs 535.4 ± 102.8 g, P < 0.001). Table 1 Characteristics and Distribution of AFD and LFD Groups Light-for-dates Infant Group (n = 16) Appropriate-for-dates Infant Group (n = 61) p value Neonate Sex Ratio (M:F) 8:8 39:22 0.31 Gestational age at birth (weeks ± SD) 38.3 ± 1.2 38.8 ± 1.1 0.14 Birth weight (g :mean ± SD) 2,264.1 ± 251.5 3,029.5 ± 383.6 < 0.001 Birth weight (SD :mean ± SD) -2.0 ± 0.5 0.11 ± 0.9 < 0.001 Birth Length (cm :mean ± SD) 45.8 ± 1.8 48.7 ± 2.2 < 0.001 Birth Length (SD :mean ± SD) -1.3 ± 0.7 -0.04 ± 0.99 < 0.001 Maternal Maternal Age (years ± SD) 34.2 ± 4.3 32.5 ± 5.5 0.22 Maternal BMI before pregnancy (kg/m² ± SD) 21.2 ± 4.7 21.1 ± 2.8 0.9 Maternal BMI at delivery (kg/m² ± SD) 24.6 ± 3.9 25.3 ± 2.4 0.47 Weight gain in pregnancy (kg) 8.3 ± 3.3 10.4 ± 2.1 0.026 Mode of Delivery: Cesarean Delivery Rate [n (%)] 4(25) 19(31.1) 0.44 Mode of Delivery: Painless labor [n (%)] 4(25) 4(6.6) 0.053 Time Since Last Meal Before Delivery (hours ± SD) 7.9 ± 4.7 8.6 ± 5.6 0.64 Placenta Placental Weight ( g ± SD) 422.3 ± 84.4 535.4 ± 102.8 < 0.001 Values are presented as mean ± SD or number (%). LFD infants showed significantly lower birth size parameters and placental weight, while maternal weight gain during pregnancy was also lower in the LFD group ( p < 0.05). Both the LFD and AFD groups exhibited maternal and neonatal comorbidities. However, Neonatal comorbidities were more frequent in the LFD group (25%, 4/16) than in the AFD group (6.5%, 4/61). Maternal comorbidities were similar between the groups. Maternal underweight was observed in both the AFD and LFD groups; however, the proportion was slightly higher in the LFD group (5 of 16 infants, 36%) than in the AFD group (13 of 61 infants, 21%) (Supplementary Table 1). Additionally, as shown in Fig. 1 , BW was significantly positively correlated with placental weight (r = 0.54, P = 3.4 x 10 − 7 ), and showed a trend of positive correlation with maternal BMI (r = 0.20, P = 0.074). However, as shown in Supplementary Fig. 1, no positive correlation was observed between BW and IGF1 levels (r = -0.23, P = 0.04) in this study. We built a random forest model using maternal information as explanatory variables to predict the binary classes LFD and AFD, with placental weight and FGR( fetal growth restriction) removed owing to their high correlation with BW. Random forest analysis identified maternal BMI as the strongest factor associated with BW (excluding placental weight), with lower maternal BMI associated with lower BW. The second most important factor was maternal height. These findings indicate that the characteristics of the study population were closely associated with maternal BMI, suggesting that maternal nutritional status was correlated with BW in this cohort (Supplementary Fig. 2). Distinct AA Profiles Observed in LFD Infants Table 2 presented a comparative analysis of the AA concentrations in the UCBs of LFD and AFD infants. Among the essential AAs (EAAs), the LFD group had significantly higher levels of methionine and phenylalanine. Similarly, among the non-EAAs, asparagine, serine, and glycine levels were significantly elevated in the LFD group. These results suggest that LFD infants display a distinct AA profile at birth compared to AFD infants. Table 2 Mean Plasma Amino Acid Levels in LFD and AFD Groups Amino acid (µM ± SD) Light-for-dates Infant Group (n = 16) Appropriate-for-dates Infant Group (n = 61) p value Cystine 7.9 ± 9.8 12.1 ± 13.3 0.27 Asparagine 80.1 ± 16.6 61.9 ± 13.3 < 0.0001 Aspartic acid 68.7 + 17.8 63.0 ± 9.6 0.23 Serine 274.7 ± 49.5 233.9 ± 60.5 0.015 Alanine 384.2 ± 76.3 383.8 ± 88.6 0.99 Glycine 415.2 ± 66.6 333.1 ± 50.6 < 0.000001 Glutamine 482.3 ± 143.7 518.0 ± 107.5 0.26 Threonine 384.4 ± 101.9 354.9 ± 62.6 0.28 Glutamic acid 171.6 ± 112.8 108.1 ± 62.7 0.045 Proline 77.0 ± 27.8 78.7 ± 20.8 0.8 Lysine 314.9 ± 69.2 286.8 ± 49.1 0.066 Histidine 106.5 ± 24.8 98.0 ± 13.1 0.2 Arginine 95.7 ± 31.7 81.9 ± 20.0 0.11 Valine 206.5 ± 48.8 182.0 ± 30.0 0.071 Methionine 33.9 ± 8.1 29.9 ± 5.8 0.026 Tyrosine 72.1 ± 19.8 63.3 ± 12.2 0.11 Isoleucine 75.9 ± 23.4 66.7 ± 14.0 0.15 Leucine 119.7 ± 37.6 113.3 ± 25.6 0.53 Phenylalanine 67.5 ± 11.1 57.9 ± 9.3 < 0.001 Tryptophan 63.0 ± 15.1 56.6 ± 8.8 0.12 Values are shown as mean ± SD. Random Forest Reveals High-Impact AAs in LFD Infants Random forest analysis was performed to identify the top 20 variables among AAs, excluding placental weight and maternal information, which most strongly influenced classification in the LFD group. Glycine, phenylalanine, asparagine, arginine, and lysine were the top five contributors (Fig. 2 ). Although arginine and lysine did not show statistically significant differences between the LFD and AFD groups (Table 2 ), they were among the top-ranked variables in the random forest analysis. Figure 3 shows the correlation between the top five AAs and BW. Consistent with the random forest findings, these AAs were elevated in infants with LFD. These results indicate that certain AAs are strongly related to BW classification, even in the absence of statistically significant group differences. Discussion In this study, we performed a comprehensive analysis of AA profiles in newborns, including those classified as LFD, using UCBs collected at birth. By incorporating maternal information and applying random forest analysis, we examined the relationship between AA levels and the birth-related parameters. In our cohort, BW was positively correlated with placental weight and maternal BMI, suggesting that fetal growth was influenced by maternal and placental nutritional status. Notably, we found little correlation between IGF-1 and BW, whereas a specific AA, such as glycine, was elevated in infants with LFD. To our knowledge, this is the first study to demonstrate an association between AA profiles and BW using random forest analyses. With advances in analytical technologies, research on AA profiles has rapidly expanded in recent years across diverse fields ( 12 – 14 ). In our recent work, we demonstrated, through both in vitro and in vivo experiments, that it is not the individual AAs themselves but rather their overall composition that directly influences glucose metabolism and growth ( 10 , 15 , 16 ). While AA measurements in UCBs have been performed for decades, our study is the first to use machine-learning-based random forests to integrate UCB AA profiles with maternal information to assess their impact on BW. Using this approach, we demonstrated that infants with LFD exhibited distinct AA profiles compared to those with AFD. It is well recognized that LFD is multifactorial, involving maternal, placental, and fetal factors, and that, in many cases, the etiology remains unclear at birth ( 4 – 6 , 17 ). Previous studies have shown that maternal BMI is positively correlated with BW, and that maternal nutritional status, BMI, and BW are positively interrelated ( 18 ). Moreover, a positive correlation has been reported between maternal BMI and placental weight, indicating that maternal nutrition, placental weight, maternal BMI, and BW are closely linked ( 19 ). In our cohort, maternal BMI, placental weight, and infant BW were strongly correlated. These findings suggest that our study population represents a group in which maternal nutritional status and BW are positively associated, reflecting the influence of maternal nutrition on fetal growth. Therefore, the aberrant AA profile observed in LFD infants may serve as a potential marker of LFD associated with maternal undernutrition. Moreover, if aberrant AA profiles can serve as markers of low nutritional status or maternal-factor–driven LFD infants, they may have substantial utility in the postnatal follow-up of LFD infants. They could contribute to clinical research on LFD attributable to undernutrition. Furthermore, comparative analyses of neonatal and maternal amino acid profiles may provide important insights that could ultimately inform targeted, more effective nutritional interventions. AAs, particularly EAAs, are indispensable for mammalian growth, and the GH–IGF axis is recognized as a major endocrine regulator ( 7 , 20 ). IGF-1 has long been recognized as a crucial factor for fetal growth. In our previous study, we demonstrated that EAA deficiency could directly suppress IGF-1 transcription and/or impair GH–IGF signaling ( 10 ). In the current study, we found no positive correlation between IGF-1 concentrations, BW, or AA levels. In contrast, AA profiles correlated with BW, suggesting that AA composition may play a more prominent role than IGF-1 in determining fetal growth. This finding emphasizes the need for further studies to elucidate the mechanisms by which AA metabolism influences intrauterine growth. Consistent with previous metabolomic studies of UCBs, we observed elevated levels of certain AAs, including phenylalanine, in the LFD group ( 20 – 23 ). These changes may reflect altered fetal amino acid metabolism due to chronic hypoxia or in utero nutrient restriction previously suggested for intrauterine growth restriction (IUGR) ( 22 , 23 ). Phenylalanine, in particular, is involved in oxidative stress and immune responses and may contribute to the pathophysiology of IUGR. Reports of elevated phenylalanine in UCB but not in maternal blood suggest a fetal metabolic rather than placental transport origin ( 22 ). Notably, glycine levels were significantly higher in the LFD group. Starvation-induced glycine elevation has been reported in both pediatric and adult populations. It may represent an adaptive mechanism to enhance insulin sensitivity, in contrast to glycine depletion typically observed in obesity and type 2 diabetes (24). Together, these findings suggest that AA alterations in LFD infants may represent coordinated fetal adaptive responses to undernutrition, potentially involving increased release from fetal tissues. This release underscores the importance of the AA balance in regulating growth and metabolic homeostasis. Moreover, alterations observed in infants with LFD may also support the DOHaD hypothesis, warranting further investigation. This study had several limitations. The number of UCBs from infants with LFD was small (n = 16), and all were obtained at a single institution, which may limit generalizability. Differences in the delivery mode (cesarean section vs vaginal delivery) and maternal complications may also have influenced the results. Finally, because maternal blood was not analyzed, it remains unclear whether the amino acid profiles measured in the cord blood originated primarily from the fetus or mother. In conclusion, our study demonstrates that LFD infants exhibit distinct AA profiles compared with AFD infants, and that these alterations are closely linked to maternal and placental factors, including maternal BMI and placental weight. These findings suggest that AA profiles on UCBs may serve as potential biomarkers of impaired fetal growth associated with maternal undernutrition or placental insufficiency. These alterations may play a role in long-term health outcomes, in alignment with the DOHaD hypothesis. Future multicenter studies with larger cohorts and paired maternal–fetal analyses are warranted to validate these findings and clarify the role of AA metabolism in fetal growth and developmental programming. Declarations Conflict of Interest: The authors declare that they have no competing interests. Ethics approval and consent to participate : The requirement for written informed consent was waived by the Ethics Committee, and an opt-out approach was used in accordance with institutional guidelines. Consent for publication : Not applicable. Availability of Data and Materials: Funding : This work was supported by a grant from the Foundation for Growth Science (FGS), FGHR Clinical Research Grant, fiscal year 2024. Author Contributions : M.A., Y.K.-S, D.Y., and F.H.conceived and designed the study. M.A., A.M., K.Y., and T.M. collected clinical data and biological samples. D.Y., and F.H. performed the AA measurements and contributed to data interpretation. D.Y. and F.H. conducted the statistical and random forest analyses. T.T. provided critical guidance and expert input. M.A., D.Y, Y.K.-S and T.T. drafted the manuscript. All authors critically reviewed the manuscript for important intellectual content and approved the final version. Acknowledgments : We thank Ms Tomomi Ueda at the University of Tokyo for their technical support in amino acids and IGF1 analysis References Okwaraji YB, Krasevec J, Bradley E, Conkle J, Stevens GA, Gatica-Domínguez G et al. National, regional, and global estimates of low birthweight in 2020, with trends from 2000: a systematic analysis. Lancet 2024; 403: 1071–1080. doi: 10.1016/S0140-6736(23)01198-4 Moriichi A, Kuwahara E, Kato N. Sustained decline in birth weight and increased rate of preterm infants born small for gestational age in Japan. Front Pediatr 2024; 12: 1480527. doi: 10.3389/fped.2024.1480527 Kato N, Sauvaget C, Yoshida H, Yokoyama T, Yoshiike N. Factors associated with birthweight decline in Japan (1980–2004). BMC Pregnancy Childbirth 2021; 21: 337. doi: 10.1186/s12884-021-03819-0 Bianchi ME, Restrepo JM. Low Birthweight as a Risk Factor for Non-communicable Diseases in Adults. Front Med (Lausanne) 2022; 8: 793990. doi: 10.3389/fmed.2021.793990 Lacagnina S. The Developmental Origins of Health and Disease (DOHaD). Am J Lifestyle Med 2020; 14: 47–50. doi: 10.1177/1559827619879694 Suzuki M, Nakano S, Murano Y, Miharada K. Low birth weight associated with a high risk of lifestyle-related diseases: fetal programing during the developmental stage. Juntendo Med J 2021; 67: JMJ21-0020-R. Rosenfeld RG. Insulin-like Growth Factors and the Basis of Growth. N Engl J Med 2003; 349: 2184–2186. doi: 10.1056/NEJMp038156 Thissen JP, Ketelslegers JM, Underwood LE. Nutritional regulation of the insulin-like growth factors. Endocr Rev 1994; 15: 80–101. doi: 10.1210/edrv-15-1-80 Nagano N, Katayama D, Hara K, Sato Y, Tanabe S, Aoki M et al. Percentile-based reference values of umbilical cord blood insulin-like growth factor 1 in Japanese newborns. J Clin Med 2022; 11: 1889. doi: 10.1210/edrv-15-1-80 Nishi H, Uchida K, Saito M, Yamanaka D, Nagata H, Tomoshige H et al. Essential amino acid intake is required for sustaining serum insulin-like growth factor-I levels but is not necessarily needed for body growth. Cells 2022; 11: 1523. doi: 10.3390/cells11091523 Mithal LB, Lancki N, Ling-Hu T, Goo YA, Otero S, Rhodes NJ et al. Evolution of the umbilical cord blood proteome across gestational development. Sci Rep 2025; 15: 2233. doi: 10.1038/s41598-024-84446-5 Kaur J, Rangra NK, Chawla PA. A comprehensive review on recent trends in amino acids detection through analytical techniques. Sep Sci Plus 2023; 6. doi: 10.1002/sscp.202300040 Kaspar H, Dettmer K, Gronwald W, Oefner PJ. Advances in amino acid analysis. Anal Bioanal Chem 2009; 393: 445–452. doi: 10.1007/s00216-008-2421-1 Lin C, Tian Q, Guo S, Xie D, Cai Y, Wang Z et al. Metabolomics for clinical biomarker discovery and therapeutic target identification. Molecules 2024; 29: 2198. doi: 10.3390/molecules29102198 Takahashi A, Furuta H, Nishi H, Kamei H, Takahashi S-I, Hakuno F. Insulin receptor substrate-2 regulates the secretion of growth factors in response to amino acid deprivation. Int J Mol Sci 2025; 26: 841. doi: 10.3390/ijms26020841 Fukushima S, Nishi H, Kumano M, Yamanaka D, Kataoka N, Hakuno F et al. A novel amino acid signaling process governs glucose-6-phosphatase transcription. iScience 2021; 24: 102778. doi: 10.1016/j.isci.2021.102778 Okui T, Nakashima N. Differences in rates of low birth weight among prefectures in Japan: an ecological study using government statistics data. Children 2022; 9: 305. doi: 10.3390/children9030305 Bhutta ZA, Das JK, Rizvi A, Gaffey MF, Walker N, Horton S et al. Evidence-based interventions for improvement of maternal and child nutrition: what can be done and at what cost? Lancet 2013; 382:452–477. doi: 10.1016/S0140-6736(13)60996-4 Strøm-Roum EM, Tanbo TG, Eskild A. The associations of maternal body mass index with birthweight and placental weight. Does maternal diabetes matter? A population study of 106 191 pregnancies. Acta Obstet Gynecol Scand. 2016;95:1162–70. doi: 10.1111/aogs.12947 Wu G. Amino acids: metabolism, functions, and nutrition. Amino Acids 2009; 37: 1–17. doi: 10.1007/s00726-009-0269-0 Vidarsdottir H, Thorkelsson T, Halldorsson TI, Bjarnason R, Geirsson RT, Rinaldo P et al. Does metabolomic profile differ with regard to birth weight? Pediatr Res 2021; 89: 1144–1151. doi: 10.1038/s41390-020-1033-0 Ivorra C, García-Vicent C, Chaves FJ, Monleón D, Morales JM, Lurbe E. Metabolomic profiling in blood from umbilical cords of low birth weight newborns. J Transl Med 2012; 10: 142. doi: 10.1186/1479-5876-10-142 Favretto D, Cosmi E, Ragazzi E, Visentin S, Tucci M, Fais P et al. Cord blood metabolomic profiling in intrauterine growth restriction. Anal Bioanal Chem 2012; 402: 1109–1121. doi: 10.1007/s00216-011-5540-z Alves A, Bassot A, Bulteau A-L, Pirola L, Morio B. Glycine Metabolism and its alterations in obesity and metabolic diseases. Nutrients 2019; 11: 1356. doi: 10.3390/nu11061356 Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Supplementalfigure1218docx.docx Supplementary Figure 1, Supplementary Table 1, Supplementary Figure 2 Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8446599","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":572226193,"identity":"42530aaa-49c9-436c-88b6-4875e315b89e","order_by":0,"name":"Yuki Kawashima-Sonoyama","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-2338-8293","institution":"Shimane University Faculty of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Kawashima-Sonoyama","suffix":""},{"id":572226194,"identity":"2997baea-ec80-4914-8aec-acc9ac69488e","order_by":1,"name":"Mako Ago","email":"","orcid":"","institution":"Shimane University Faculty of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mako","middleName":"","lastName":"Ago","suffix":""},{"id":572226195,"identity":"9ccb9860-7213-4807-a891-3cf1534a9b6f","order_by":2,"name":"Aisa Moriyama","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Aisa","middleName":"","lastName":"Moriyama","suffix":""},{"id":572226196,"identity":"78861d4a-63d3-40a2-a70e-89702bfa8d4f","order_by":3,"name":"Kei Yamamoto","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kei","middleName":"","lastName":"Yamamoto","suffix":""},{"id":572226197,"identity":"1d977683-593a-4228-b916-2ec962c181f5","order_by":4,"name":"Toshiko Minamoto","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Toshiko","middleName":"","lastName":"Minamoto","suffix":""},{"id":572226198,"identity":"bc4ed1cc-6006-4969-8349-370cbdabceb0","order_by":5,"name":"Daisuke Yamanaka","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Yamanaka","suffix":""},{"id":572226199,"identity":"47f02872-a25f-4cab-8438-00b4ac7cd7b3","order_by":6,"name":"Fumihiko Hakuno","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Fumihiko","middleName":"","lastName":"Hakuno","suffix":""},{"id":572226200,"identity":"72a46509-5eca-4003-bf71-2efc74462c6c","order_by":7,"name":"Takeshi Taketani","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Takeshi","middleName":"","lastName":"Taketani","suffix":""}],"badges":[],"createdAt":"2025-12-25 06:00:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8446599/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8446599/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100400093,"identity":"b16d840f-ee96-454f-ac2c-c0136e36e330","added_by":"auto","created_at":"2026-01-16 11:57:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142111,"visible":true,"origin":"","legend":"","description":"","filename":"Maniscrpt251225docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/770989d4dee316d75f88cf1d.docx"},{"id":100400420,"identity":"010b260c-3e8d-4b9b-a5b1-779b18731154","added_by":"auto","created_at":"2026-01-16 11:58:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23663,"visible":true,"origin":"","legend":"","description":"","filename":"Table1225.docx","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/f10a93c60e7e77e3e51c8630.docx"},{"id":100400603,"identity":"c9400893-19cd-4584-bb2b-2ef809eed332","added_by":"auto","created_at":"2026-01-16 11:58:19","extension":"json","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8471,"visible":true,"origin":"","legend":"","description":"","filename":"251567.json","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/f80f10e43db904ce5865d734.json"},{"id":100399758,"identity":"54deb238-fd22-4dad-ac5c-3a1be4ab08d8","added_by":"auto","created_at":"2026-01-16 11:57:34","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":283710,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfigure1218docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/900f6c198d78d4f6fe5b7ca8.docx"},{"id":100399801,"identity":"938dd1f4-3890-4887-a5b1-f02d76ac060a","added_by":"auto","created_at":"2026-01-16 11:57:37","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86007,"visible":true,"origin":"","legend":"","description":"","filename":"2515670enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/fdad273be305ed92b5ff9e66.xml"},{"id":100399836,"identity":"2162426e-a90c-4560-8fba-bda0901b77dd","added_by":"auto","created_at":"2026-01-16 11:57:38","extension":"pptx","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":438238,"visible":true,"origin":"","legend":"","description":"","filename":"Figure251106.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/647b9e1e7bfe36888bf38bf0.pptx"},{"id":100400382,"identity":"6e3b4ea6-4f7f-40e3-9505-e77fbf0a5086","added_by":"auto","created_at":"2026-01-16 11:58:07","extension":"xml","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83225,"visible":true,"origin":"","legend":"","description":"","filename":"2515670structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/b7a4e288df03dd72e718b98e.xml"},{"id":100400602,"identity":"3201ba45-0773-4d4f-b76c-c13f64a4bd3a","added_by":"auto","created_at":"2026-01-16 11:58:19","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94845,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/405a052d7bf2017ae2b8f988.html"},{"id":100399753,"identity":"029d3ed6-6a5b-479a-bde2-1531d8014a62","added_by":"auto","created_at":"2026-01-16 11:57:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":461650,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation of birth weight with maternal and placental factors.\u003c/strong\u003e\u003cbr\u003e\n(A) Scatter plot showing the correlation between birth weight (standard deviation, SD) and maternal body mass index (BMI).\u003cbr\u003e\n(B) Scatter plot showing the correlation between birth weight (SD) and placental weight.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/618e28f85e0b8a41d429f5c9.png"},{"id":100399584,"identity":"9a41a601-3aa4-45d8-875e-71a8fec40356","added_by":"auto","created_at":"2026-01-16 11:57:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":293418,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRandom forest analysis of factors influencing birth weight classification.\u003c/strong\u003e\u003cbr\u003e\nVariable importance plot showing the top 20 predictors of classification into the light-for-dates group.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/eec5bc61d3dbbbe3345c85fd.png"},{"id":100399689,"identity":"1c47b52d-5d4b-41bb-8499-cf20c08caf93","added_by":"auto","created_at":"2026-01-16 11:57:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":550547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations of key amino acids with birth weight.\u003c/strong\u003e\u003cbr\u003e\nScatter plots showing the correlations between birth weight (SD) and (A) glycine, (B) arginine, (C)phenylalanine, (D)lysine, and (E) asparagine.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/333c88a76d37fc2a79cc0b85.png"},{"id":104400348,"identity":"eee1dbe0-6237-4c01-8be8-31f53e3aeedf","added_by":"auto","created_at":"2026-03-11 12:09:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2070826,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/66e67efb-b5e0-46ef-8206-eefad52dd96c.pdf"},{"id":100399796,"identity":"2ee1db13-84a4-4dcf-b65e-3d8bf133ef78","added_by":"auto","created_at":"2026-01-16 11:57:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":283710,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Figure 1, Supplementary Table 1, Supplementary Figure 2\u003c/p\u003e","description":"","filename":"Supplementalfigure1218docx.docx","url":"https://assets-eu.researchsquare.com/files/rs-8446599/v1/1a501d15e84fbeecd49e3619.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Associations of Umbilical Cord Blood Amino Acid Profiles and Insulin-Like Growth Factor 1 With Birth Weight","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLow birth weight (LBW; BW\u0026thinsp;\u0026lt;\u0026thinsp;2,500 g) is a significant global health concern because it increases the risk of neonatal mortality, impairs child growth and neurodevelopment, and increases the likelihood of developing chronic diseases in adulthood (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In Japan, the total number of live births has steadily declined over the past few decades; however, the proportion of LBW infants has continued to increase (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). One of the major contributing factors to this phenomenon is the rising prevalence of undernutrition among pregnant women (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Consequently, LBW infants face an increased risk of postnatal growth impairment and higher susceptibility to adult-onset metabolic disorders, including type 2 diabetes, hypertension, and chronic kidney disease (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This concept, widely known as the Developmental Origins of Health and Disease (DOHaD), proposes that an individual\u0026rsquo;s future health and disease risks are strongly influenced by the intrauterine and early postnatal environments (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Therefore, growing attention has been directed toward elucidating the mechanisms underlying LBW associated with maternal undernutrition, along with efforts to establish preventive and therapeutic approaches (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInsulin-like growth factor 1 (IGF-1) is a crucial regulator of fetal and postnatal growth (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While growth hormone primarily stimulates IGF-1 production, IGF-1 production is also closely linked to nutritional status (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Previous studies have shown a positive correlation between BW and IGF-1 levels in umbilical cord blood (UCB) (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Recently, we discovered that different amino acid (AA) profiles can influence IGF-1 secretion, indicating a potential regulatory mechanism between fetal nutrition and growth factor activity in rodents and hepatocyte-derived cell lines (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, the detailed relationships between specific nutrient profiles, such as AA composition and IGF-1 concentrations, have not yet been fully elucidated in human samples.\u003c/p\u003e \u003cp\u003eUCB serves as a valuable window into the intrauterine nutritional environment, reflecting both maternal and placental influences on fetal development (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). AAs are essential nutrients for protein synthesis, growth, and maintenance of physiological functions. Essential and conditionally essential AAs must be obtained exclusively from the diet and are therefore considered indicators of nutritional status (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Therefore, analyzing AA profiles in UCBs and their associations with IGF-1 levels, BW, and maternal parameters may offer novel insights into the nutritional mechanisms underlying fetal growth.\u003c/p\u003e \u003cp\u003eIn this study, we investigated AA profiles in UCB from newborns and examined their associations with BW, IGF-1 levels, and maternal factors. Additionally, to clarify the metabolic adaptations associated with fetal growth restriction, we performed a random forest analysis to identify key AAs that differentiate LBW infants from their normal-BW counterparts.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis prospective, single-center, observational study was conducted at the Department of Pediatrics, Shimane University Hospital, Japan.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eThe eligible participants were neonates born at Shimane University Hospital between 37\u0026thinsp;+\u0026thinsp;0 and 41\u0026thinsp;+\u0026thinsp;6 weeks of gestation, and their mothers. Inclusion was determined based on the availability of UCB immediately after birth, and written informed consent was obtained from the mothers. Stillbirths were excluded from the study. Infants classified as light-for-dates (LFD) were defined as those with BWs below the 10th percentile for gestational age (regardless of length), whereas appropriate-for-dates (AFD) infants had BWs above the 10th percentile. Among the various definitions for LBW infants, we used the LFD criterion to increase the number of eligible cases in this study.\u003c/p\u003e\n\u003ch3\u003eSampling of UCBs and Analysis of AAs and IGF-1\u003c/h3\u003e\n\u003cp\u003eUCB (2.5 mL) was collected from each neonate during delivery using a plastic syringe. Blood samples were processed to separate serum and plasma, centrifuged at 1,200 \u0026times; g for 10 min. The supernatant was collected, and the serum was decanted; serum samples were stored at \u0026minus;\u0026thinsp;20\u0026deg;C until use. Plasma AA profiles were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Serum levels of growth-related hormones, including IGF-1, were also measured (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eClinical and Demographic Data\u003c/h3\u003e\n\u003cp\u003eWe obtained information for mothers and newborns during the perinatal period, including maternal characteristics (age, pregnancy weight and height, body mass index (BMI), complications, medication use, pre-delivery fasting time, and newborn data (gestational age, BW, birth length, head circumference, Apgar scores, mode of delivery, and placental weight). Details regarding the timing of the last meal and intravenous fluid administration during labor were also recorded.\u003c/p\u003e\n\u003ch3\u003eStatistical and Machine Learning Analyses\u003c/h3\u003e\n\u003cp\u003eThe collected data, including AA profiles, IGF-1 levels, and clinical parameters, were subjected to statistical and random forest analyses to identify characteristic patterns associated with LBW. In the statistical analysis, the Pearson correlation coefficient (r) values and the associated t or \u003cem\u003eP\u003c/em\u003e values were calculated using Excel (Microsoft Corporation) as follows: r value\u0026thinsp;=\u0026thinsp;CORREL (Array1, Array2), t value = |r * \u0026radic;(N \u0026ndash; 2) / \u0026radic;(1 \u0026ndash; r\u003csup\u003e2\u003c/sup\u003e)|, \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;TDIST (t, N \u0026minus;\u0026thinsp;2, 2). CORREL and TDIST are functions in Excel. Array1 and Array2 were composed of BW, IGF-1, amino acids, or maternal factors. N is the number of samples. Random forest analysis was performed using Python 3.12.11, scikit-learn 1.6.1, NumPy 2.0.2, and Pandas 2.2.2. Other dependencies followed the default Google Colaboratory runtime version at the time of the analysis. We trained scikit-learn\u0026rsquo;s random forest classifier using umbilical cord blood amino acid concentrations as predictors and two-class labels (AFD and LFD) as the binary outcomes. The models were fitted using the following settings: n_estimators\u0026thinsp;=\u0026thinsp;10, max_depth\u0026thinsp;=\u0026thinsp;None, bootstrap\u0026thinsp;=\u0026thinsp;False, min_samples_leaf\u0026thinsp;=\u0026thinsp;2, min_samples_split\u0026thinsp;=\u0026thinsp;2, max_features = 'sqrt,\u0026rsquo; and random_state\u0026thinsp;=\u0026thinsp;0. We also described impurity-based (Gini) feature importance.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan. The study protocol was approved by the Medical Research Ethics Committee of the Shimane University Faculty of Medicine (Approval Number: KS20220904-1). Written informed consent was obtained from the guardians of all participants. All personal data were anonymized prior to analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study participants\u003c/h2\u003e \u003cp\u003eThe characteristics of the study participants are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Table\u0026nbsp;1. A total of 16 and 61 infants were included in the LFD and AFD groups, respectively. The gestational age at birth was similar between the groups. As expected, BW, birth length, and their SD scores were significantly lower in the LFD group than in the AFD group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Maternal age and BMI before pregnancy and at delivery did not differ between the groups. Maternal gestational weight gain was significantly lower in the LFD group (8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 kg) than that in the AFD group (10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 kg, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026). The rates of cesarean delivery and painless labor were not significantly different, although painless labor tended to be more frequent in the LFD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.053). The interval between the last meal and delivery was comparable between the groups. Placental weight was significantly lower in the LFD group than in the AFD group (422.3\u0026thinsp;\u0026plusmn;\u0026thinsp;84.4 g vs 535.4\u0026thinsp;\u0026plusmn;\u0026thinsp;102.8 g, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eCharacteristics and Distribution of AFD and LFD Groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLight-for-dates Infant Group (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAppropriate-for-dates Infant Group (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eNeonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex Ratio (M:F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8:8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39:22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGestational age at birth (weeks\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirth weight\u003c/p\u003e \u003cp\u003e(g :mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,264.1\u0026thinsp;\u0026plusmn;\u0026thinsp;251.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,029.5\u0026thinsp;\u0026plusmn;\u0026thinsp;383.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirth weight\u003c/p\u003e \u003cp\u003e(SD :mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirth Length\u003c/p\u003e \u003cp\u003e(cm :mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBirth Length\u003c/p\u003e \u003cp\u003e(SD :mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eMaternal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaternal Age\u003c/p\u003e \u003cp\u003e(years\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaternal BMI before pregnancy (kg/m\u0026sup2; \u0026plusmn; SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaternal BMI at delivery (kg/m\u0026sup2; \u0026plusmn; SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeight gain in pregnancy (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMode of Delivery:\u003c/p\u003e \u003cp\u003eCesarean Delivery Rate [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMode of Delivery:\u003c/p\u003e \u003cp\u003ePainless labor [n (%)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTime Since Last Meal Before Delivery\u003c/p\u003e \u003cp\u003e(hours\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlacenta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlacental Weight\u003c/p\u003e \u003cp\u003e( g\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e422.3\u0026thinsp;\u0026plusmn;\u0026thinsp;84.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e535.4\u0026thinsp;\u0026plusmn;\u0026thinsp;102.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or number (%). LFD infants showed significantly lower birth size parameters and placental weight, while maternal weight gain during pregnancy was also lower in the LFD group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBoth the LFD and AFD groups exhibited maternal and neonatal comorbidities. However, Neonatal comorbidities were more frequent in the LFD group (25%, 4/16) than in the AFD group (6.5%, 4/61). Maternal comorbidities were similar between the groups. Maternal underweight was observed in both the AFD and LFD groups; however, the proportion was slightly higher in the LFD group (5 of 16 infants, 36%) than in the AFD group (13 of 61 infants, 21%) (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eAdditionally, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, BW was significantly positively correlated with placental weight (r\u0026thinsp;=\u0026thinsp;0.54, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.4 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e), and showed a trend of positive correlation with maternal BMI (r\u0026thinsp;=\u0026thinsp;0.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.074). However, as shown in Supplementary Fig.\u0026nbsp;1, no positive correlation was observed between BW and IGF1 levels (r = -0.23, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe built a random forest model using maternal information as explanatory variables to predict the binary classes LFD and AFD, with placental weight and FGR(\u003cb\u003efetal growth restriction)\u003c/b\u003e removed owing to their high correlation with BW. Random forest analysis identified maternal BMI as the strongest factor associated with BW (excluding placental weight), with lower maternal BMI associated with lower BW. The second most important factor was maternal height. These findings indicate that the characteristics of the study population were closely associated with maternal BMI, suggesting that maternal nutritional status was correlated with BW in this cohort (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDistinct AA Profiles Observed in LFD Infants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented a comparative analysis of the AA concentrations in the UCBs of LFD and AFD infants. Among the essential AAs (EAAs), the LFD group had significantly higher levels of methionine and phenylalanine. Similarly, among the non-EAAs, asparagine, serine, and glycine levels were significantly elevated in the LFD group. These results suggest that LFD infants display a distinct AA profile at birth compared to AFD infants.\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\u003eMean Plasma Amino Acid Levels in LFD and AFD Groups\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=\"char\" char=\"\u0026plusmn;\" 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 \u003cp\u003eAmino acid (\u0026micro;M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLight-for-dates Infant Group (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAppropriate-for-dates Infant Group (n\u0026thinsp;=\u0026thinsp;61)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsparagine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e61.9\u0026thinsp;\u0026plusmn;\u0026thinsp;13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.7\u0026thinsp;+\u0026thinsp;17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e274.7\u0026thinsp;\u0026plusmn;\u0026thinsp;49.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e233.9\u0026thinsp;\u0026plusmn;\u0026thinsp;60.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384.2\u0026thinsp;\u0026plusmn;\u0026thinsp;76.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e383.8\u0026thinsp;\u0026plusmn;\u0026thinsp;88.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e415.2\u0026thinsp;\u0026plusmn;\u0026thinsp;66.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e333.1\u0026thinsp;\u0026plusmn;\u0026thinsp;50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e482.3\u0026thinsp;\u0026plusmn;\u0026thinsp;143.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e518.0\u0026thinsp;\u0026plusmn;\u0026thinsp;107.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThreonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e384.4\u0026thinsp;\u0026plusmn;\u0026thinsp;101.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e354.9\u0026thinsp;\u0026plusmn;\u0026thinsp;62.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlutamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.6\u0026thinsp;\u0026plusmn;\u0026thinsp;112.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e108.1\u0026thinsp;\u0026plusmn;\u0026thinsp;62.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e78.7\u0026thinsp;\u0026plusmn;\u0026thinsp;20.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e314.9\u0026thinsp;\u0026plusmn;\u0026thinsp;69.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e286.8\u0026thinsp;\u0026plusmn;\u0026thinsp;49.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.5\u0026thinsp;\u0026plusmn;\u0026thinsp;24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e98.0\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArginine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.7\u0026thinsp;\u0026plusmn;\u0026thinsp;31.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e81.9\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206.5\u0026thinsp;\u0026plusmn;\u0026thinsp;48.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e182.0\u0026thinsp;\u0026plusmn;\u0026thinsp;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e29.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoleucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.9\u0026thinsp;\u0026plusmn;\u0026thinsp;23.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e66.7\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.7\u0026thinsp;\u0026plusmn;\u0026thinsp;37.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e113.3\u0026thinsp;\u0026plusmn;\u0026thinsp;25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e57.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\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\u003eTryptophan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.0\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are shown as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRandom Forest Reveals High-Impact AAs in LFD Infants\u003c/h2\u003e \u003cp\u003eRandom forest analysis was performed to identify the top 20 variables among AAs, excluding placental weight and maternal information, which most strongly influenced classification in the LFD group. Glycine, phenylalanine, asparagine, arginine, and lysine were the top five contributors (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Although arginine and lysine did not show statistically significant differences between the LFD and AFD groups (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), they were among the top-ranked variables in the random forest analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the correlation between the top five AAs and BW. Consistent with the random forest findings, these AAs were elevated in infants with LFD. These results indicate that certain AAs are strongly related to BW classification, even in the absence of statistically significant group differences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we performed a comprehensive analysis of AA profiles in newborns, including those classified as LFD, using UCBs collected at birth. By incorporating maternal information and applying random forest analysis, we examined the relationship between AA levels and the birth-related parameters. In our cohort, BW was positively correlated with placental weight and maternal BMI, suggesting that fetal growth was influenced by maternal and placental nutritional status. Notably, we found little correlation between IGF-1 and BW, whereas a specific AA, such as glycine, was elevated in infants with LFD. To our knowledge, this is the first study to demonstrate an association between AA profiles and BW using random forest analyses.\u003c/p\u003e \u003cp\u003eWith advances in analytical technologies, research on AA profiles has rapidly expanded in recent years across diverse fields (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). In our recent work, we demonstrated, through both in vitro and in vivo experiments, that it is not the individual AAs themselves but rather their overall composition that directly influences glucose metabolism and growth (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). While AA measurements in UCBs have been performed for decades, our study is the first to use machine-learning-based random forests to integrate UCB AA profiles with maternal information to assess their impact on BW. Using this approach, we demonstrated that infants with LFD exhibited distinct AA profiles compared to those with AFD. It is well recognized that LFD is multifactorial, involving maternal, placental, and fetal factors, and that, in many cases, the etiology remains unclear at birth (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Previous studies have shown that maternal BMI is positively correlated with BW, and that maternal nutritional status, BMI, and BW are positively interrelated (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Moreover, a positive correlation has been reported between maternal BMI and placental weight, indicating that maternal nutrition, placental weight, maternal BMI, and BW are closely linked (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In our cohort, maternal BMI, placental weight, and infant BW were strongly correlated. These findings suggest that our study population represents a group in which maternal nutritional status and BW are positively associated, reflecting the influence of maternal nutrition on fetal growth. Therefore, the aberrant AA profile observed in LFD infants may serve as a potential marker of LFD associated with maternal undernutrition. Moreover, if aberrant AA profiles can serve as markers of low nutritional status or maternal-factor\u0026ndash;driven LFD infants, they may have substantial utility in the postnatal follow-up of LFD infants. They could contribute to clinical research on LFD attributable to undernutrition. Furthermore, comparative analyses of neonatal and maternal amino acid profiles may provide important insights that could ultimately inform targeted, more effective nutritional interventions.\u003c/p\u003e \u003cp\u003eAAs, particularly EAAs, are indispensable for mammalian growth, and the GH\u0026ndash;IGF axis is recognized as a major endocrine regulator (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). IGF-1 has long been recognized as a crucial factor for fetal growth. In our previous study, we demonstrated that EAA deficiency could directly suppress IGF-1 transcription and/or impair GH\u0026ndash;IGF signaling (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In the current study, we found no positive correlation between IGF-1 concentrations, BW, or AA levels. In contrast, AA profiles correlated with BW, suggesting that AA composition may play a more prominent role than IGF-1 in determining fetal growth. This finding emphasizes the need for further studies to elucidate the mechanisms by which AA metabolism influences intrauterine growth.\u003c/p\u003e \u003cp\u003eConsistent with previous metabolomic studies of UCBs, we observed elevated levels of certain AAs, including phenylalanine, in the LFD group (\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). These changes may reflect altered fetal amino acid metabolism due to chronic hypoxia or in utero nutrient restriction previously suggested for intrauterine growth restriction (IUGR) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Phenylalanine, in particular, is involved in oxidative stress and immune responses and may contribute to the pathophysiology of IUGR. Reports of elevated phenylalanine in UCB but not in maternal blood suggest a fetal metabolic rather than placental transport origin (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Notably, glycine levels were significantly higher in the LFD group. Starvation-induced glycine elevation has been reported in both pediatric and adult populations. It may represent an adaptive mechanism to enhance insulin sensitivity, in contrast to glycine depletion typically observed in obesity and type 2 diabetes (24). Together, these findings suggest that AA alterations in LFD infants may represent coordinated fetal adaptive responses to undernutrition, potentially involving increased release from fetal tissues. This release underscores the importance of the AA balance in regulating growth and metabolic homeostasis. Moreover, alterations observed in infants with LFD may also support the DOHaD hypothesis, warranting further investigation.\u003c/p\u003e \u003cp\u003eThis study had several limitations. The number of UCBs from infants with LFD was small (n\u0026thinsp;=\u0026thinsp;16), and all were obtained at a single institution, which may limit generalizability. Differences in the delivery mode (cesarean section vs vaginal delivery) and maternal complications may also have influenced the results. Finally, because maternal blood was not analyzed, it remains unclear whether the amino acid profiles measured in the cord blood originated primarily from the fetus or mother.\u003c/p\u003e \u003cp\u003eIn conclusion, our study demonstrates that LFD infants exhibit distinct AA profiles compared with AFD infants, and that these alterations are closely linked to maternal and placental factors, including maternal BMI and placental weight. These findings suggest that AA profiles on UCBs may serve as potential biomarkers of impaired fetal growth associated with maternal undernutrition or placental insufficiency. These alterations may play a role in long-term health outcomes, in alignment with the DOHaD hypothesis. Future multicenter studies with larger cohorts and paired maternal\u0026ndash;fetal analyses are warranted to validate these findings and clarify the role of AA metabolism in fetal growth and developmental programming.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e: The requirement for written informed consent was waived by the Ethics Committee, and an opt-out approach was used in accordance with institutional guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e: This work was supported by a grant from the Foundation for Growth Science (FGS), FGHR Clinical Research Grant, fiscal year 2024.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e: M.A., Y.K.-S, D.Y., and F.H.conceived and designed the study.\u003cbr\u003e\u0026nbsp;M.A., A.M., K.Y., and T.M. collected clinical data and biological samples.\u003cbr\u003e\u0026nbsp;D.Y., and F.H. performed the AA measurements and contributed to data interpretation. D.Y. and F.H. conducted the statistical and random forest analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eT.T. provided critical guidance and expert input. M.A., D.Y, Y.K.-S and T.T. drafted the manuscript.\u003cbr\u003e\u0026nbsp;All authors critically reviewed the manuscript for important intellectual content and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eWe thank Ms Tomomi Ueda at the University of Tokyo for their technical support in amino acids and IGF1 analysis\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOkwaraji YB, Krasevec J, Bradley E, Conkle J, Stevens GA, Gatica-Dom\u0026iacute;nguez G et al. National, regional, and global estimates of low birthweight in 2020, with trends from 2000: a systematic analysis. Lancet 2024; 403: 1071\u0026ndash;1080. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(23)01198-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(23)01198-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoriichi A, Kuwahara E, Kato N. Sustained decline in birth weight and increased rate of preterm infants born small for gestational age in Japan. Front Pediatr 2024; 12: 1480527. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fped.2024.1480527\u003c/span\u003e\u003cspan address=\"10.3389/fped.2024.1480527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato N, Sauvaget C, Yoshida H, Yokoyama T, Yoshiike N. Factors associated with birthweight decline in Japan (1980\u0026ndash;2004). BMC Pregnancy Childbirth 2021; 21: 337. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12884-021-03819-0\u003c/span\u003e\u003cspan address=\"10.1186/s12884-021-03819-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBianchi ME, Restrepo JM. Low Birthweight as a Risk Factor for Non-communicable Diseases in Adults. Front Med (Lausanne) 2022; 8: 793990. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2021.793990\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2021.793990\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLacagnina S. The Developmental Origins of Health and Disease (DOHaD). Am J Lifestyle Med 2020; 14: 47\u0026ndash;50. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1559827619879694\u003c/span\u003e\u003cspan address=\"10.1177/1559827619879694\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki M, Nakano S, Murano Y, Miharada K. Low birth weight associated with a high risk of lifestyle-related diseases: fetal programing during the developmental stage. Juntendo Med J 2021; 67: JMJ21-0020-R.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenfeld RG. Insulin-like Growth Factors and the Basis of Growth. N Engl J Med 2003; 349: 2184\u0026ndash;2186. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1056/NEJMp038156\u003c/span\u003e\u003cspan address=\"10.1056/NEJMp038156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThissen JP, Ketelslegers JM, Underwood LE. Nutritional regulation of the insulin-like growth factors. Endocr Rev 1994; 15: 80\u0026ndash;101. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/edrv-15-1-80\u003c/span\u003e\u003cspan address=\"10.1210/edrv-15-1-80\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagano N, Katayama D, Hara K, Sato Y, Tanabe S, Aoki M et al. Percentile-based reference values of umbilical cord blood insulin-like growth factor 1 in Japanese newborns. J Clin Med 2022; 11: 1889. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1210/edrv-15-1-80\u003c/span\u003e\u003cspan address=\"10.1210/edrv-15-1-80\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishi H, Uchida K, Saito M, Yamanaka D, Nagata H, Tomoshige H et al. Essential amino acid intake is required for sustaining serum insulin-like growth factor-I levels but is not necessarily needed for body growth. Cells 2022; 11: 1523. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/cells11091523\u003c/span\u003e\u003cspan address=\"10.3390/cells11091523\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMithal LB, Lancki N, Ling-Hu T, Goo YA, Otero S, Rhodes NJ et al. Evolution of the umbilical cord blood proteome across gestational development. Sci Rep 2025; 15: 2233. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-84446-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-84446-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaur J, Rangra NK, Chawla PA. A comprehensive review on recent trends in amino acids detection through analytical techniques. Sep Sci Plus 2023; 6. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/sscp.202300040\u003c/span\u003e\u003cspan address=\"10.1002/sscp.202300040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaspar H, Dettmer K, Gronwald W, Oefner PJ. Advances in amino acid analysis. Anal Bioanal Chem 2009; 393: 445\u0026ndash;452. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00216-008-2421-1\u003c/span\u003e\u003cspan address=\"10.1007/s00216-008-2421-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin C, Tian Q, Guo S, Xie D, Cai Y, Wang Z et al. Metabolomics for clinical biomarker discovery and therapeutic target identification. Molecules 2024; 29: 2198. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/molecules29102198\u003c/span\u003e\u003cspan address=\"10.3390/molecules29102198\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi A, Furuta H, Nishi H, Kamei H, Takahashi S-I, Hakuno F. Insulin receptor substrate-2 regulates the secretion of growth factors in response to amino acid deprivation. Int J Mol Sci 2025; 26: 841. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms26020841\u003c/span\u003e\u003cspan address=\"10.3390/ijms26020841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFukushima S, Nishi H, Kumano M, Yamanaka D, Kataoka N, Hakuno F et al. A novel amino acid signaling process governs glucose-6-phosphatase transcription. iScience 2021; 24: 102778. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.isci.2021.102778\u003c/span\u003e\u003cspan address=\"10.1016/j.isci.2021.102778\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkui T, Nakashima N. Differences in rates of low birth weight among prefectures in Japan: an ecological study using government statistics data. Children 2022; 9: 305. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/children9030305\u003c/span\u003e\u003cspan address=\"10.3390/children9030305\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhutta ZA, Das JK, Rizvi A, Gaffey MF, Walker N, Horton S et al. Evidence-based interventions for improvement of maternal and child nutrition: what can be done and at what cost? Lancet 2013; 382:452\u0026ndash;477. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(13)60996-4\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(13)60996-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStr\u0026oslash;m-Roum EM, Tanbo TG, Eskild A. The associations of maternal body mass index with birthweight and placental weight. Does maternal diabetes matter? A population study of 106 191 pregnancies. Acta Obstet Gynecol Scand. 2016;95:1162\u0026ndash;70. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/aogs.12947\u003c/span\u003e\u003cspan address=\"10.1111/aogs.12947\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu G. Amino acids: metabolism, functions, and nutrition. Amino Acids 2009; 37: 1\u0026ndash;17. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00726-009-0269-0\u003c/span\u003e\u003cspan address=\"10.1007/s00726-009-0269-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVidarsdottir H, Thorkelsson T, Halldorsson TI, Bjarnason R, Geirsson RT, Rinaldo P et al. Does metabolomic profile differ with regard to birth weight? Pediatr Res 2021; 89: 1144\u0026ndash;1151. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41390-020-1033-0\u003c/span\u003e\u003cspan address=\"10.1038/s41390-020-1033-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvorra C, Garc\u0026iacute;a-Vicent C, Chaves FJ, Monle\u0026oacute;n D, Morales JM, Lurbe E. Metabolomic profiling in blood from umbilical cords of low birth weight newborns. J Transl Med 2012; 10: 142. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/1479-5876-10-142\u003c/span\u003e\u003cspan address=\"10.1186/1479-5876-10-142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFavretto D, Cosmi E, Ragazzi E, Visentin S, Tucci M, Fais P et al. Cord blood metabolomic profiling in intrauterine growth restriction. Anal Bioanal Chem 2012; 402: 1109\u0026ndash;1121. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00216-011-5540-z\u003c/span\u003e\u003cspan address=\"10.1007/s00216-011-5540-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlves A, Bassot A, Bulteau A-L, Pirola L, Morio B. Glycine Metabolism and its alterations in obesity and metabolic diseases. Nutrients 2019; 11: 1356. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu11061356\u003c/span\u003e\u003cspan address=\"10.3390/nu11061356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8446599/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8446599/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate whether umbilical cord blood (UCB) amino acid profiles are affected by newborn birth weight (BW), insulin-like growth factor 1 (IGF-1), and maternal/placental factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a prospective, single-center, observational study using UCBs from 16 light-for-date (LFD) and 61 appropriate-for-dates (AFD) infants. The 20 amino acids in UCBs were measured using liquid chromatography-tandem mass spectrometry. Random forest analysis identified factors influencing the amino acid (AA) profile and BW.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult\u003c/strong\u003e\u003cbr\u003e\nBW was positively correlated with maternal body mass index and placental weight but not with IGF-1. LFD infants had higher levels of glycine, phenylalanine, methionine, and asparagine than AFD infants. Random forest analysis identified glycine, phenylalanine, asparagine, arginine, and lysine as the top contributors to LFD or AFD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003cbr\u003e\nAlthough IGF-1 levels were similar, AA profiles differed from those of AFD infants, suggesting that profiling may identify LFD infants beyond IGF-1 levels.\u003c/p\u003e","manuscriptTitle":"Associations of Umbilical Cord Blood Amino Acid Profiles and Insulin-Like Growth Factor 1 With Birth Weight","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 08:50:53","doi":"10.21203/rs.3.rs-8446599/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"512a9c09-bc75-4bc6-a516-d4348bea441d","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60897475,"name":"Health sciences/Health care/Paediatrics"},{"id":60897476,"name":"Health sciences/Biomarkers/Predictive markers"}],"tags":[],"updatedAt":"2026-03-02T15:47:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 08:50:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8446599","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8446599","identity":"rs-8446599","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}