Kim TW, Park SS, Park HS. Effects of exercise training during advanced maternal age on the cognitive function of offspring. Int J Mol Sci. 2022. https://doi.org/10.3390/ijms23105517.
Cao J, Xu W, Liu Y, Zhang B, Zhang Y, Yu T, et al. Trends in maternal age and the relationship between advanced age and adverse pregnancy outcomes: a population-based register study in wuhan, china, 2010–2017. Public Health. 2022;206:8–14.
Huete-Garcia A, Otaola-Barranquero M. Demographic assessment of down syndrome: a systematic review. Int J Environ Res Public Health. 2021. https://doi.org/10.3390/ijerph18010352.
Elci G, Cakmak A, Elci E, Sayan S. The effect of advanced maternal age on perinatal outcomes in nulliparous pregnancies. J Perinat Med. 2022;50(8):1087–95.
Kim H, Kim MS, Seo Y, Yum SK. Short-term outcomes of very-low-birth-weight infants born to mothers of advanced and very advanced maternal age. J Matern Fetal Neonatal Med. 2022. https://doi.org/10.1080/14767058.2022.2065192.
Saccone G, Gragnano E, Ilardi B, Marrone V, Strina I, Venturella R, et al. Maternal and perinatal complications according to maternal age: a systematic review and meta-analysis. Int J Gynaecol Obstet. 2022;159(1):43–55.
Bardanzellu F, Fanos V. The choice of amniotic fluid in metabolomics for the monitoring of fetus health – update. Expert Rev Proteomics. 2019;16(6):487–99.
Shan J, Xie T, Xu J, Zhou H, Zhao X. Metabolomics of the amniotic fluid: is it a feasible approach to evaluate the safety of Chinese medicine during pregnancy? J Appl Toxicol. 2019;39(1):163–71.
Crosland BA, Hedges MA, Ryan KS, D’Mello RJ, Mccarty O, Malhotra SV, et al. Amniotic fluid: its role in fetal development and beyond. J Perinatol. 2025. https://doi.org/10.1038/s41372-025-02313-1.
Gonzalez-Riano C, Santos M, Diaz M, Garcia-Beltran C, Lerin C, Barbas C, et al. Birth weight and early postnatal outcomes: association with the cord blood lipidome. Nutrients. 2022. https://doi.org/10.3390/nu14183760.
Dogan B, Karaer A, Tuncay G, Tecellioglu N, Mumcu A. High-resolution (1)H-NMR spectroscopy indicates variations in metabolomics profile of follicular fluid from women with advanced maternal age. J Assist Reprod Genet. 2020;37(2):321–30.
Huang Y, Tu M, Qian Y, Ma J, Chen L, Liu Y, et al. Age-dependent metabolomic profile of the follicular fluids from women undergoing assisted reproductive technology treatment. Front Endocrinol (Lausanne). 2022;13:818888.
Gu Y, Zhang X, Wang R, Wei Y, Peng H, Wang K, et al. Metabolomic profiling of exosomes reveals age-related changes in ovarian follicular fluid. Eur J Med Res. 2024;29(1):4.
He XL, Hu XJ, Luo BY, Xia YY, Zhang T, Saffery R, et al. The effects of gestational diabetes mellitus with maternal age between 35 and 40 years on the metabolite profiles of plasma and urine. BMC Pregnancy Childbirth. 2022;22(1):174.
Trohl J, Schindler M, Buske M, de Nivelle J, Toto NA, Navarrete SA. Advanced maternal age leads to changes within the insulin/igf system and lipid metabolism in the reproductive tract and preimplantation embryo: insights from the rabbit model. Mol Hum Reprod. 2023;29(12).
Xie Y, Peng G, Zhao H, Scharfe C. Association of maternal age and blood markers for metabolic disease in newborns. Metabolites. 2023. https://doi.org/10.3390/metabo14010005.
Hudobenko J, Di Gesu CM, Mooz PR, Petrosino J, Putluri N, Ganesh BP, et al. Maternal dysbiosis produces long-lasting behavioral changes in offspring. Mol Psychiatry. 2025;30(5):1847–58.
Mao H, Xu Y, Lu F, Ma C, Zhu S, Li G, et al. An integrative multi-omics approach reveals metabolic mechanism of flavonoids during anaerobic fermentation of de’ang pickled tea. Food Chem X. 2024;24:102021.
Dickinson JM, Drummond MJ, Coben JR, Volpi E, Rasmussen BB. Aging differentially affects human skeletal muscle amino acid transporter expression when essential amino acids are ingested after exercise. Clin Nutr. 2013;32(2):273–80.
Wang Y, Wu P, Huang Y, Ye Y, Yang X, Sun F, et al. BMI and lipidomic biomarkers with risk of gestational diabetes in pregnant women. Obesity. 2022;30(10):2044–54.
Heath H, Degreef K, Rosario R, Smith M, Mitchell I, Pilolla K, et al. Identification of potential biomarkers and metabolic insights for gestational diabetes prevention: A review of evidence contrasting gestational diabetes versus weight loss studies that May direct future nutritional metabolomics studies. Nutrition. 2023;107:111898.
Lee SM, Kang Y, Lee EM, Jung YM, Hong S, Park SJ, et al. Metabolomic biomarkers in midtrimester maternal plasma can accurately predict the development of preeclampsia. Sci Rep. 2020;10(1):16142.
Jin D, Zhao S, Li H, Xia Z, Che M, Huang R, et al. Plasma acylcarnitine and diabetic retinopathy: a study from Eastern China. Front Endocrinol (Lausanne). 2022;13:977428.
Jain R, Wade G, Ong I, Chaurasia B, Simcox J. Determination of tissue contributions to the circulating lipid pool in cold exposure via systematic assessment of lipid profiles. J Lipid Res. 2022;63(7):100197.
O’Neill K, Alexander J, Azuma R, Xiao R, Snyder NW, Mesaros CA, et al. Gestational diabetes alters the metabolomic profile in 2nd trimester amniotic fluid in a sex-specific manner. Int J Mol Sci. 2018. https://doi.org/10.3390/ijms19092696.
Menon R, Jones J, Gunst PR, Kacerovsky M, Fortunato SJ, Saade GR, et al. Amniotic fluid metabolomic analysis in spontaneous preterm birth. Reprod Sci. 2014;21(6):791–803.
Skinner AM, Narchi H. Preterm nutrition and neurodevelopmental outcomes. World J Methodol. 2021;11(6):278–93.
Zhao Y, Tan G, Fu H, Ding Y, Guo X, Zhang Y. The inositol 1,4,5-trisphosphate receptor type 2 protein domains regulate calcium levels and ion transport gene expression in laying ducks’ uterus. Poult Sci. 2025;104(10):105520.
Weston E, Pangilinan F, Eaton S, Orford M, Leung KY, Copp AJ, et al. Investigating genetic determinants of plasma inositol status in adult humans. J Nutr. 2022;152(11):2333–42.
Ye X, Baker PN, Tong C. The updated understanding of advanced maternal age. Fundam Res. 2024;4(6):1719–28.
Li Y, Sun Y, Zhang X, Wang X, Yang P, Guan X, et al. Relationship between amniotic fluid metabolic profile with fetal gender, maternal age, and gestational week. BMC Pregnancy Childbirth. 2021;21(1):638.
Patel N, Hellmuth C, Uhl O, Godfrey K, Briley A, Welsh P, et al. Cord metabolic profiles in obese pregnant women: insights into offspring growth and body composition. J Clin Endocrinol Metab. 2018;103(1):346–55.
Yong-Ping L, Reichetzeder C, Prehn C, Yin LH, Chu C, Elitok S, et al. Impact of maternal smoking associated lyso-phosphatidylcholine 20:3 on offspring brain development. J Steroid Biochem Mol Biol. 2020;199:105591.
Hoffman MC, Olincy A, D’Alessandro A, Reisz JA, Hansen KC, Hunter SK et al. Effects of phosphatidylcholine and betaine supplements on women’s serum choline. J Nutr Intermed Metab. 2019;16.
Cao T, Zhao J, Hong X, Wang G, Pearson C, Adams WG, et al. Cord blood plasma metabolome-wide associations with height from birth to adolescence. J Bone Miner Res. 2023;38(5):707–18.
Yang F, Chen G. The nutritional functions of dietary sphingomyelin and its applications in food. Front Nutr. 2022;9:1002574.
Wang X, Liu J, Hui X, Song Y. Metabolomics applied to cord serum in preeclampsia newborns: implications for neonatal outcomes. Front Pediatr. 2022;10:869381.
Yagci ZB, Esvap E, Ozkara HA, Ulgen KO, Olmez EO. Inflammatory response and its relation to sphingolipid metabolism proteins: chaperones as potential indirect anti-inflammatory agents. Adv Protein Chem Struct Biol. 2019;114:153–219.
Jutanom M, Higaki C, Yamashita S, Nakagawa K, Matsumoto S, Kinoshita M. Effects of sphingolipid fractions from golden oyster mushroom (Pleurotus citrinopileatus) on apoptosis induced by inflammatory stress in an intestinal tract in vitro model. J Oleo Sci. 2020;69(9):1087–93.
Olzynska A, Delcroix P, Dolejsova T, Krzaczek K, Korchowiec B, Czogalla A, et al. Properties of lipid models of lung surfactant containing cholesterol and oxidized lipids: A mixed experimental and computational study. Langmuir. 2020;36(4):1023–33.
Raj JU, Bland RD, Bhattacharya J, Rabinovitch M, Matthay MA. Life-saving effect of pulmonary surfactant in premature babies. J Clin Invest. 2024. https://doi.org/10.1172/JCI179948.
Fang H, Wang W, Wang L, Zhu J, Lin W, Deng H, et al. Lipidomic profiling of amniotic fluid reveals aberrant fetal lung development and fetal growth disrupted by lipid disorders during gestational asthma. J Pharm Biomed Anal. 2025;252:116475.
Zhai X, Liu J, Yu M, Zhang Q, Li M, Zhao N, et al. Nontargeted metabolomics reveals the potential mechanism underlying the association between birthweight and metabolic disturbances. BMC Pregnancy Childbirth. 2023;23(1):14.
Antonopoulou G, Magkrioti C, Chatzidaki I, Nastos D, Grammenoudi S, Bozonelos K, et al. Generation of new knock-out mouse strains of lysophosphatidic acid receptor 1. Int J Mol Sci. 2025. https://doi.org/10.3390/ijms26062811.
Kurusu S, Terashima R, Sugiyama M, Tanaka M, Kadowaki T, Kizaki K, et al. Expression of lysophosphatidic acid receptors in the rat uterus: cellular distribution of protein and gestation-associated changes in gene expression. J Vet Med Sci. 2023;85(11):1165–71.
Fujii T, Nagamatsu T, Schust DJ, Ichikawa M, Kumasawa K, Yabe S, et al. Placental expression of lysophosphatidic acid receptors in normal pregnancy and preeclampsia. Am J Reprod Immunol. 2019;82(5):e13176.
Byeon SK, Khanam R, Rahman S, Hasan T, Rizvi S, Madugundu AK, et al. Maternal serum lipidomics identifies lysophosphatidic acid as a predictor of small for gestational age neonates. Mol Omics. 2021;17(6):956–66.
Moros G, Boutsikou T, Fotakis C, Iliodromiti Z, Sokou R, Katsila T, et al. Insights into intrauterine growth restriction based on maternal and umbilical cord blood metabolomics. Sci Rep. 2021;11(1):7824.
Kadakia R, Talbot O, Kuang A, Bain JR, Muehlbauer MJ, Stevens RD, et al. Cord blood metabolomics: association with newborn anthropometrics and C-peptide across ancestries. J Clin Endocrinol Metab. 2019;104(10):4459–72.
Lu YP, Reichetzeder C, Prehn C, von Websky K, Slowinski T, Chen YP, et al. Fetal serum metabolites are independently associated with gestational diabetes mellitus. Cell Physiol Biochem. 2018;45(2):625–38.
Mansell T, Vlahos A, Collier F, Ponsonby AL, Vuillermin P, Ellul S, et al. The newborn metabolome: associations with gestational diabetes, sex, gestation, birth mode, and birth weight. Pediatr Res. 2022;91(7):1864–73.
Zhang F, Tong L, Shi C, Zuo R, Wang L, Wang Y. Deep learning in predicting preterm birth: a comparative study of machine learning algorithms. Maternal-Fetal Medicine. 2024;6(3):141–6.
Xie J, Jiang Y, Zhou Y, Jin D, Lu X, Ge Y. Hierarchical classification of factors associated with noninvasive prenatal testing failures and its impact on pregnancy outcomes. Maternal-Fetal Medicine. 2024;6(4):215–24.