{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T03:33:04Z","timestamp":1793503984868,"version":"4.5.0"},"reference-count":40,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T00:00:00Z","timestamp":1756339200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006769","name":"Russian Science Foundation (RSF)","doi-asserted-by":"publisher","award":["24-64-00006"],"award-info":[{"award-number":["24-64-00006"]}],"id":[{"id":"10.13039\/501100006769","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJMS"],"abstract":"<jats:p>Gestational diabetes mellitus (GDM) is a growing global health concern, driving the need for novel diagnostic and prognostic approaches. The aim of this study was to analyze the amino acid profile in the mother\u2013fetus system (maternal venous blood, umbilical cord blood, and amniotic fluid) and to identify specific biological markers of GDM and macrosomia. Using HPLC-MS\/MS, we analyzed serum from maternal venous and umbilical cord blood, along with amniotic fluid, across 94 mother\u2013fetus pairs (53 GDM, 41 controls). Machine learning and metabolic pathway analysis revealed significant alterations in 19 amino acids. In GDM, maternal serum showed elevated 5-OH-lysine and homocitrulline, while cord blood had higher isoleucine, serine, and threonine. Amniotic fluid exhibited increased leucine, isoleucine, threonine, serine, arginine, and ornithine. Conversely, histidine, glutamine, alanine, asparagine, \u03b2-\/\u03b3-aminobutyric acids, phenylalanine, ornithine, and citrulline were reduced. Histidine, glutamine, and asparagine inversely correlated with blood glucose (r = \u22120.26, r = \u22120.33, r = \u22120.30) and were lower in GDM. These findings highlight three key metabolic loci in GDM pathogenesis, with glutamine, histidine, and asparagine emerging as potential maternal blood biomarkers for early macrosomia prediction. However, given confounding factors in metabolomic studies, further large-scale validation is essential.<\/jats:p>","DOI":"10.3390\/ijms26178351","type":"journal-article","created":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T08:01:01Z","timestamp":1756454461000},"page":"8351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Amino Acid Profile Alterations in the Mother\u2013Fetus System in Gestational Diabetes Mellitus and Macrosomia"],"prefix":"10.3390","volume":"26","author":[{"given":"Natalia. A.","family":"Frankevich","sequence":"first","affiliation":[{"name":"National Medical Research Center for Obstetrics Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alisa. O.","family":"Tokareva","sequence":"additional","affiliation":[{"name":"National Medical Research Center for Obstetrics Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergey. Yu.","family":"Yuriev","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Siberian State Medical University, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vitaly. V.","family":"Chagovets","sequence":"additional","affiliation":[{"name":"National Medical Research Center for Obstetrics Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia"},{"name":"Department of Obstetrics and Gynecology, Siberian State Medical University, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasia. A.","family":"Kutsenko","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology, Siberian State Medical University, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasia. V.","family":"Novoselova","sequence":"additional","affiliation":[{"name":"National Medical Research Center for Obstetrics Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tamara. E.","family":"Karapetian","sequence":"additional","affiliation":[{"name":"National Medical Research Center for Obstetrics Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vadim. V.","family":"Lagutin","sequence":"additional","affiliation":[{"name":"National Medical Research Center for Obstetrics Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladimir. E.","family":"Frankevich","sequence":"additional","affiliation":[{"name":"National Medical Research Center for Obstetrics Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia"},{"name":"Laboratory of Translational Medicine, Siberian State Medical University, 634050 Tomsk, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gennady. T.","family":"Sukhikh","sequence":"additional","affiliation":[{"name":"National Medical Research Center for Obstetrics Gynecology and Perinatology Named After Academician V.I. Kulakov, Ministry of Healthcare of Russian Federation, 117997 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109050","DOI":"10.1016\/j.diabres.2021.109050","article-title":"IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group\u2019s Criteria","volume":"183","author":"Wang","year":"2022","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1001\/jama.2017.3635","article-title":"Association of gestational weight gain with maternal and infant outcomes: A systematic review and meta-analysis","volume":"317","author":"Goldstein","year":"2017","journal-title":"JAMA"},{"key":"ref_3","first-page":"93","article-title":"Perinatal Outcomes Among Cases of Predicted and Unpredicted Macrosomia","volume":"29","author":"Isaku","year":"2023","journal-title":"Gynecol. Obstet. Reprod. Med."},{"key":"ref_4","first-page":"CD000938","article-title":"Induction of labour at or near term for suspected fetal macrosomia","volume":"2023","author":"Boulvain","year":"2023","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1159\/000371628","article-title":"Gestational diabetes mellitus and macrosomia: A literature review","volume":"66","author":"Kamana","year":"2015","journal-title":"Ann. Nutr. Metab."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1038\/s41597-024-03536-1","article-title":"Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data","volume":"11","author":"Gunaratne","year":"2024","journal-title":"Sci. Data"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1159\/000509902","article-title":"Gestational Diabetes Mellitus and Developmental Programming","volume":"76","author":"Chu","year":"2021","journal-title":"Ann. Nutr. Metab."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dalfr\u00e0, M.G., Burlina, S., Del Vescovo, G.G., and Lapolla, A. (2020). Genetics and Epigenetics: New Insight on Gestational Diabetes Mellitus. Front. Endocrinol., 11.","DOI":"10.3389\/fendo.2020.602477"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1097\/JCMA.0000000000000894","article-title":"Maternal factors associated with fetal macrosomia","volume":"86","author":"Liu","year":"2023","journal-title":"J. Chin. Med. Assoc."},{"key":"ref_10","first-page":"127","article-title":"A retrospective identification of risk factors associated with fetal macrosomia","volume":"26","author":"Moodley","year":"2022","journal-title":"Afr. J. Reprod. Health"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"S608","DOI":"10.1016\/j.ajog.2018.01.010","article-title":"Fetal Growth: Evaluation and Management","volume":"218","author":"Romero","year":"2018","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1002\/jcu.23238","article-title":"Can we improve the diagnosis of fetal macrosomia?","volume":"50","author":"Rizzo","year":"2022","journal-title":"J. Clin. Ultrasound"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.pedneo.2023.09.007","article-title":"The environmental risk factors associated with fetal macrosomia: An umbrella review","volume":"65","author":"Jenabi","year":"2024","journal-title":"Pediatr. Neonatol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1111\/1471-0528.17802","article-title":"Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review","volume":"131","author":"Ewington","year":"2024","journal-title":"BJOG"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1054\/mehy.2001.1450","article-title":"Streptomycin revisited: Molecular action in the microbial cell","volume":"58","author":"Kornder","year":"2002","journal-title":"Med. Hypotheses"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1126\/science.150.3701.1290","article-title":"Amino Acid Changes Provoked by Streptomycin in a Polypeptide Synthesized in vitro","volume":"150","author":"Old","year":"1965","journal-title":"Science"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.metabol.2019.01.017","article-title":"The tRNA-associated dysregulation in diabetes mellitus","volume":"94","author":"Zhou","year":"2019","journal-title":"Metabolism"},{"key":"ref_18","first-page":"4596127","article-title":"Leukotriene involvement in the insulin receptor pathway and macrophage profiles in muscles from type 1 diabetic mice","volume":"2019","author":"Filgueiras","year":"2019","journal-title":"Mediat. Inflamm."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e41913","DOI":"10.1097\/MD.0000000000041913","article-title":"Association between gamma-glutamyl transferase and diabetes factors among elderly nonobese individuals","volume":"104","author":"He","year":"2025","journal-title":"Medicine"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"0300060520937911","DOI":"10.1177\/0300060520937911","article-title":"Non-linear relationship between gamma-glutamyl transferase and type 2 diabetes mellitus risk: Secondary analysis of a prospective cohort study","volume":"48","author":"Wang","year":"2020","journal-title":"J. Int. Med. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.clnu.2025.03.008","article-title":"Plasma amino acid profiles and gestational diabetes mellitus risk: A case-control study","volume":"48","author":"Hong","year":"2025","journal-title":"Clin. Nutr."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kong, X., Zhu, Q., Dong, Y., Li, Y., Liu, J., Yan, Q., Huang, M., and Niu, Y. (2022). Analysis of serum fatty acid, amino acid, and organic acid profiles in gestational hypertension and gestational diabetes mellitus via targeted metabolomics. Front. Nutr., 9.","DOI":"10.3389\/fnut.2022.974902"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1089\/met.2014.0113","article-title":"Plasma levels of lysine, tyrosine, and valine during pregnancy are independent risk factors of insulin resistance and gestational diabetes","volume":"13","author":"Park","year":"2015","journal-title":"Metab. Syndr. Relat. Disord."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s40200-016-0283-1","article-title":"Amino acid profiling in the gestational diabetes mellitus","volume":"16","author":"Rahimi","year":"2017","journal-title":"J. Diabetes Metab. Disord."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"750","DOI":"10.2174\/157016111797484062","article-title":"Functional link between adenosine and insulin: A hypothesis for fetoplacental vascular endothelial dysfunction in gestational diabetes","volume":"9","author":"Belmar","year":"2011","journal-title":"Curr. Vasc. Pharmacol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.ajog.2004.08.011","article-title":"Maternal and fetal amino acid concentrations in normal pregnancies and in pregnancies with gestational diabetes mellitus","volume":"192","author":"Cetin","year":"2005","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1210\/jc.2015-2862","article-title":"Maternal early pregnancy serum metabolites and risk of gestational diabetes mellitus","volume":"100","author":"Enquobahrie","year":"2015","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"158","DOI":"10.2337\/dc13-0989","article-title":"Metabolomics reveals broad-scale metabolic perturbations in hyperglycemic mothers during pregnancy","volume":"37","author":"Scholtens","year":"2014","journal-title":"Diabetes Care"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Alesi, S., Ghelani, D., Rassie, K., and Mousa, A. (2021). Molecular Sciences Metabolomic Biomarkers in Gestational Diabetes Mellitus: A Review of the Evidence. Int. J. Mol. Sci, 22.","DOI":"10.3390\/ijms22115512"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.molmet.2018.08.002","article-title":"L-Alanine activates hepatic AMP-activated protein kinase and modulates systemic glucose metabolism","volume":"17","author":"Adachi","year":"2018","journal-title":"Mol. Metab."},{"key":"ref_31","first-page":"687","article-title":"Data visualization and feature selection: New algorithms for nongaussian data","volume":"12","author":"Yang","year":"2000","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1007\/s10115-013-0679-x","article-title":"Explaining prediction models and individual predictions with feature contributions","volume":"41","author":"Kononenko","year":"2014","journal-title":"Knowl. Inf. Syst."},{"key":"ref_33","unstructured":"Kam Ho, T. (1995, January 14\u201316). Random Decision Forests. Proceedings of the 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1093\/nar\/gkab1128","article-title":"ConsensusPathDB 2022: Molecular interactions update as a resource for network biology","volume":"50","author":"Kamburov","year":"2022","journal-title":"Nucleic Acids Res."},{"key":"ref_35","unstructured":"R Core Team (2021). A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100819","DOI":"10.1016\/j.softx.2021.100819","article-title":"Praznik: High performance information-based feature selection","volume":"16","author":"Kursa","year":"2021","journal-title":"SoftwareX"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wickham, H. (2008). Elegant Graphics for Data Analysis: Ggplot2, Springer.","DOI":"10.1007\/978-0-387-98141-3"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v021.i12","article-title":"Reshaping data with the reshape package","volume":"21","author":"Wickham","year":"2007","journal-title":"J. Stat. Softw."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v077.i01","article-title":"Ranger: A fast implementation of random forests for high dimensional data in C++ and R","volume":"77","author":"Wright","year":"2017","journal-title":"J. Stat. Softw."},{"key":"ref_40","first-page":"3457","article-title":"Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression","volume":"Volume 130","author":"Banerjee","year":"2021","journal-title":"Proceedings of the 24th International Conference on Artificial Intelligence and Statistics"}],"container-title":["International Journal of Molecular Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/17\/8351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:34:31Z","timestamp":1760034871000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1422-0067\/26\/17\/8351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,28]]},"references-count":40,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["ijms26178351"],"URL":"https:\/\/doi.org\/10.3390\/ijms26178351","relation":{},"ISSN":["1422-0067"],"issn-type":[{"value":"1422-0067","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,28]]}}}