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Cited by 52Gestational Diabetes Research and ManagementMetabolomics and Mass Spectrometry StudiesDiabetes, Cardiovascular Risks, and Lipoproteins

Molecular Biomarkers for Gestational Diabetes Mellitus and Postpartum Diabetes

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Lu, Wenqian, Hu, Cheng·2022·Chinese Medical Journal
DOI 10.1097/cm9.0000000000002160PMC PMC9746787
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Abstract

ABSTRACT: Gestational diabetes mellitus (GDM) is a growing public health problem worldwide that threatens both maternal and fetal health. Identifying individuals at high risk for GDM and diabetes after GDM is particularly useful for early intervention and prevention of disease progression. In the last decades, a number of studies have used metabolomics, genomics, and proteomic approaches to investigate associations between biomolecules and GDM progression. These studies clearly demonstrate that various biomarkers reflect pathological changes in GDM. The established markers have potential use as screening and diagnostic tools in GDM and in postpartum diabetes research. In the present review, we summarize recent studies of metabolites, single-nucleotide polymorphisms, microRNAs, and proteins associated with GDM and its transition to postpartum diabetes, with a focus on their predictive value in screening and diagnosis.

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  • Circulating miRNAs dysregulated in GDM are implicated in inflammation, endothelial dysfunction, lipid and glucose metabolism, and insulin signaling.

    “Several miRNAs dysregulated in GDM have been implicated in inflammation, endothelial dysfunction, lipid and glucose metabolism, and insulin signaling.”

    From Maternal Overnutrition and Fetal Programming: Long-Term Metabolic, Cognitive, and Epigenetic Consequences (2026) · cites this paper as [41]

    The “miRNAs in GDM” section reports dysregulated miRNAs associated with inflammation, endothelial dysfunction, and fatty-acid and glucose metabolism. It also discusses insulin resistance and secretion, but does not establish an effect on insulin signaling specifically. Although it identifies some dysregulated serum miRNAs, the mechanisms described are not all shown for circulating miRNAs; page numbers were not provided in our copy. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Elevated BCAA levels are associated with insulin resistance, mitochondrial stress, and dysregulated mTOR signaling.

    “Elevated BCAA levels are associated with insulin resistance, mitochondrial stress, and dysregulated mTOR signaling, pathways that are critically involved in placental nutrient sensing and fetal metabolic programming.”

    From Maternal Overnutrition and Fetal Programming: Long-Term Metabolic, Cognitive, and Epigenetic Consequences (2026) · cites this paper as [41]

    In “Metabolomics in GDM” (page not provided), the review says BCAAs are associated with insulin resistance and describes BCAA activation of the mTOR pathway as a possible explanation. It presents β-cell mitochondrial dysfunction as a proposed consequence of toxic BCAA metabolites, not an established association between elevated BCAA levels and mitochondrial stress. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Placental and circulating miRNAs can modulate gene expression through epigenetic mechanisms and may alter key developmental pathways including PPAR, mTOR, WNT, and insulin/IGF signaling.

    “Importantly, placental and circulating miRNAs can modulate gene expression through epigenetic mechanisms and may alter key developmental pathways, including PPAR, mTOR, WNT, and insulin/IGF signaling, thereby influencing fetal growth patterns and long-term disease risk [41,43,44,45].”

    From Maternal Overnutrition and Fetal Programming: Long-Term Metabolic, Cognitive, and Epigenetic Consequences (2026) · cites this paper as [41]

    The 'miRNAs in GDM' section describes placental and serum miRNAs regulating target-gene expression, including miR-518d inhibiting PPAR-α and miR-29b affecting trophoblast activity. It does not establish miRNA effects on mTOR, WNT, or insulin/IGF signaling, or link those pathways to fetal growth patterns and long-term disease risk. No page numbers were supplied for the indexed passages. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • GDM is associated with molecular biomarkers including metabolites, genetic variants, microRNAs, and circulating proteins that reflect maternal metabolic dysfunction and may influence fetal programming.

    “GDM is associated with a range of molecular biomarkers, including metabolites, genetic variants, microRNAs (miRNAs), and circulating proteins, which collectively reflect maternal metabolic dysfunction and may influence fetal programming processes.”

    From Maternal Overnutrition and Fetal Programming: Long-Term Metabolic, Cognitive, and Epigenetic Consequences (2026) · cites this paper as [41]

    The Introduction (page unavailable) lists metabolites, SNPs, miRNAs, and proteins as GDM biomarkers, and Challenges and Future Directions (page unavailable) links them to glucolipid metabolism, insulin resistance, and inflammation. The Introduction mentions a transgenerational cycle of disease, but the passages do not establish that these biomarkers influence fetal programming. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Metabolomic studies of GDM consistently report alterations in circulating metabolites, particularly branched-chain amino acids.

    “Metabolomic studies of GDM have consistently reported alterations in circulating metabolites, particularly branched-chain amino acids (BCAAs).”

    From Maternal Overnutrition and Fetal Programming: Long-Term Metabolic, Cognitive, and Epigenetic Consequences (2026) · cites this paper as [41]

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  1. [1]American Diabetes Association. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2020. Diabetes Care 2020; 43: S14– S31. doi: 10.2337/dc20-S002. 31862745 · DOI · Paper page
  2. [3]Mutsaerts MA Groen H Buiter-Van der Meer A Sijtsma A Sauer PJ Land JA. Effects of paternal and maternal lifestyle factors on pregnancy complications and perinatal outcome. A population-based birth-cohort study: The GECKO Drenthe cohort. Hum Reprod 2014; 29: 824– 834. doi: 10.1093/humrep/deu006. 24510962 · DOI
  3. [4]Mattsson K Källen K Longnecker MP Rignell-Hydbom A Rylander L. Maternal smoking during pregnancy and daughters’ risk of gestational diabetes and obesity. Diabetologia 2013; 56: 1689– 1695. doi: 10.1007/s00125-013-2936-7. 23699990 PMC3701733 · DOI
  4. [5]Zhang C Sundaram R Maisog J Calafat AM Barr DB Buck Louis GM. A prospective study of prepregnancy serum concentrations of perfluorochemicals and the risk of gestational diabetes. Fertil Steril 2015; 103: 184– 189. doi: 10.1016/j.fertnstert.2014.10.001. 25450302 PMC4282598 · DOI · Paper page
  5. [6]McIntyre HD Catalano P Zhang C Desoye G Mathiesen ER Damm P. Gestational diabetes mellitus. Nat Rev Dis Primers 2019; 5: 47 doi: 10.1038/s41572-019-0098-8. 31296866 · DOI · Paper page
  6. [7]Mitanchez D Yzydorczyk C Siddeek B Boubred F Benahmed M Simeoni U. The offspring of the diabetic mother - short- and long-term implications. Best Pract Res Clin Obstet Gynaecol 2015; 29: 256– 269. doi: 10.1016/j.bpobgyn.2014.08.004. 25267399 · DOI
  7. [9]Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001; 69: 89– 95. doi: 10.1067/mcp.2001.113989. 11240971 · DOI
  8. [10]Smirnakis KV Plati A Wolf M Thadhani R Ecker JL. Predicting gestational diabetes: choosing the optimal early serum marker. Am J Obstet Gynecol 2007; 196: e1– e6. doi: 10.1016/j.ajog.2006.12.011. 17403439 · DOI
  9. [11]Kwak SH Kim SH Cho YM Go MJ Cho YS Choi SH. A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes 2012; 61: 531– 541. doi: 10.2337/db11-1034. 22233651 PMC3266417 · DOI
  10. [12]Poirier C Desgagne V Guerin R Bouchard L. MicroRNAs in pregnancy and gestational diabetes mellitus: emerging role in maternal metabolic regulation. Curr Diab Rep 2017; 17: 35 doi: 10.1007/s11892-017-0856-5. 28378294 · DOI
  11. [13]Nicholson JK Lindon JC. Systems biology: metabonomics. Nature 2008; 455: 1054– 1056. doi: 10.1038/4551054a. 18948945 · DOI
  12. [14]Mao X Chen X Chen C Zhang H Law KP. Metabolomics in gestational diabetes. Clin Chim Acta 2017; 475: 116– 127. doi: 10.1016/j.cca.2017.10.019. 29066210 · DOI
  13. [15]Chen Q Francis E Hu G Chen L. Metabolomic profiling of women with gestational diabetes mellitus and their offspring: Review of metabolomics studies. J Diabetes Complications 2018; 32: 512– 523. doi: 10.1016/j.jdiacomp.2018.01.007. 29506818 · DOI
  14. [16]Johnson CH Ivanisevic J Siuzdak G. Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 2016; 17: 451– 459. doi: 10.1038/nrm.2016.25. 26979502 PMC5729912 · DOI · Paper page
  15. [18]Cetin I de Santis MS Taricco E Radaelli T Teng C Ronzoni S. Maternal and fetal amino acid concentrations in normal pregnancies and in pregnancies with gestational diabetes mellitus. Am J Obstet Gynecol 2005; 192: 610– 617. doi: 10.1016/j.ajog.2004.08.011. 15696011 · DOI
  16. [19]Leitner M Fragner L Danner S Holeschofsky N Leitner K Tischler S. Combined metabolomic analysis of plasma and urine reveals AHBA, tryptophan and serotonin metabolism as potential risk factors in gestational diabetes mellitus (GDM). Front Mol Biosci 2017; 4: 84 doi: 10.3389/fmolb.2017.00084. 29312952 PMC5742855 · DOI
  17. [20]Guo X Rotter JI. Genome-wide association studies. JAMA 2019; 322: 1705– 1706. doi: 10.1001/jama.2019.16479. 31688871 · DOI
  18. [21]Lee JE. High-throughput genotyping. Forum Nutr 2007; 60: 97– 101. doi: 10.1159/000107078. 17684405 · DOI
  19. [22]Kim S Misra A. SNP genotyping: technologies and biomedical applications. Annu Rev Biomed Eng 2007; 9: 289– 320. doi: 10.1146/annurev.bioeng.9.060906.152037. 17391067 · DOI
  20. [23]Ding C Jin S. High-throughput methods for SNP genotyping. Methods Mol Biol 2009; 578: 245– 254. doi: 10.1007/978-1-60327-411-1_16. 19768599 · DOI
  21. [24]Bartel DP. MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004; 116: 281– 297. doi: 10.1016/s0092-8674(04)00045-5. 14744438 · DOI
  22. [25]Finnegan EF Pasquinelli AE. MicroRNA biogenesis: regulating the regulators. Crit Rev Biochem Mol Biol 2013; 48: 51– 68. doi: 10.3109/10409238.2012.738643. 23163351 PMC3557704 · DOI
  23. [26]Lee Y Jeon K Lee JT Kim S Kim VN. MicroRNA maturation: stepwise processing and subcellular localization. EMBO J 2002; 21: 4663– 4670. doi: 10.1093/emboj/cdf476. 12198168 PMC126204 · DOI
  24. [27]Wander PL Boyko EJ Hevner K Parikh VJ Tadesse MG Sorensen TK. Circulating early- and mid-pregnancy microRNAs and risk of gestational diabetes. Diabetes Res Clin Pract 2017; 132: 1– 9. doi: 10.1016/j.diabres.2017.07.024. 28783527 PMC5623075 · DOI
  25. [28]Sebastiani G Guarino E Grieco GE Formichi C Delli Poggi C Ceccarelli E. Circulating microRNA (miRNA) expression profiling in plasma of patients with gestational diabetes mellitus reveals upregulation of miRNA miR-330-3p. Front Endocrinol (Lausanne) 2017; 8: 345 doi: 10.3389/fendo.2017.00345. 29312141 PMC5732927 · DOI
  26. [29]Pheiffer C Dias S Rheeder P Adam S. Decreased expression of circulating miR-20a-5p in South African women with gestational diabetes mellitus. Mol Diagn Ther 2018; 22: 345– 352. doi: 10.1007/s40291-018-0325-0. 29556924 · DOI
  27. [30]Saliminejad K Khorram Khorshid HR Soleymani Fard S Ghaffari SH. An overview of microRNAs: biology, functions, therapeutics, and analysis methods. J Cell Physiol 2019; 234: 5451– 5465. doi: 10.1002/jcp.27486. 30471116 · DOI
  28. [31]Pritchard CC Cheng HH Tewari M. MicroRNA profiling: approaches and considerations. Nat Rev Genet 2012; 13: 358– 369. doi: 10.1038/nrg3198. 22510765 PMC4517822 · DOI · Paper page
  29. [32]Cheng Y Dong L Zhang J Zhao Y Li Z. Recent advances in microRNA detection. Analyst 2018; 143: 1758– 1774. doi: 10.1039/C7AN02001E. 29560992 · DOI
  30. [33]Zöllner H Hahn SA Maghnouj A. Quantitative RT-PCR specific for precursor and mature miRNAs. Methods Mol Biol 2014; 1095: 121– 134. doi: 10.1007/978-1-62703-703-7_10. 24166308 · DOI
  31. [34]Kappel A Keller A. miRNA assays in the clinical laboratory: workflow, detection technologies and automation aspects. Clin Chem Lab Med 2017; 55: 636– 647. doi: 10.1515/cclm-2016-0467. 27987355 · DOI
  32. [35]Benes V Castoldi M. Expression profiling of microRNA using real-time quantitative PCR, how to use it and what is available. Methods 2010; 50: 244– 249. doi: 10.1016/j.ymeth.2010.01.026. 20109550 · DOI · Paper page
  33. [36]Patterson SD Aebersold RH. Proteomics: the first decade and beyond. Nat Genet 2003; 33: (Suppl): 311– 323. doi: 10.1038/ng1106. 12610541 · DOI
  34. [37]Rabilloud T Chevallet M Luche S Lelong C. Two-dimensional gel electrophoresis in proteomics: past, present and future. J Proteomics 2010; 73: 2064– 2077. doi: 10.1016/j.jprot.2010.05.016. 20685252 · DOI
  35. [38]Tannu NS Hemby SE. Two-dimensional fluorescence difference gel electrophoresis for comparative proteomics profiling. Nat Protoc 2006; 1: 1732– 1742. doi: 10.1038/nprot.2006.256. 17487156 PMC2001252 · DOI
  36. [39]Ong SE Blagoev B Kratchmarova I Kristensen DB Steen H Pandey A. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 2002; 1: 376– 386. doi: 10.1074/mcp.m200025-mcp200. 12118079 · DOI · Paper page
  37. [41]Pan S Aebersold R Chen R Rush J Goodlett DR McIntosh MW. Mass spectrometry based targeted protein quantification: methods and applications. J Proteome Res 2009; 8: 787– 797. doi: 10.1021/pr800538n. 19105742 PMC2657955 · DOI
  38. [42]Yates JR Ruse CI Nakorchevsky A. Proteomics by mass spectrometry: approaches, advances, and applications. Annu Rev Biomed Eng 2009; 11: 49– 79. doi: 10.1146/annurev-bioeng-061008-124934. 19400705 · DOI · Paper page
  39. [43]Aebersold R Mann M. Mass spectrometry-based proteomics. Nature 2003; 422: 198– 207. doi: 10.1038/nature01511. 12634793 · DOI · Paper page
  40. [44]Kim SM Park JS Norwitz ER Lee SM Kim BJ Park CW. Identification of proteomic biomarkers in maternal plasma in the early second trimester that predict the subsequent development of gestational diabetes. Reprod Sci 2012; 19: 202– 209. doi: 10.1177/1933719111417889. 22101237 · DOI
  41. [45]Fruscalzo A Londero AP Driul L Henze A Tonutti L Ceraudo M. First trimester concentrations of the TTR-RBP4-retinol complex components as early markers of insulin-treated gestational diabetes mellitus. Clin Chem Lab Med 2015; 53: 1643– 1651. doi: 10.1515/cclm-2014-0929. 25781688 · DOI · Paper page
  42. [46]Gika H Theodoridis G. Sample preparation prior to the LC-MS-based metabolomics/metabonomics of blood-derived samples. Bioanalysis 2011; 3: 1647– 1661. doi: 10.4155/bio.11.122. 21756097 · DOI
  43. [47]Enquobahrie DA Denis M Tadesse MG Gelaye B Ressom HW Williams MA. Maternal early pregnancy serum metabolites and risk of gestational diabetes mellitus. J Clin Endocrinol Metab 2015; 100: 4348– 4356. doi: 10.1210/jc.2015-2862. 26406294 PMC4702451 · DOI
  44. [48]Roy C Tremblay PY Anassour-Laouan-Sidi E Lucas M Forest JC Giguere Y. Risk of gestational diabetes mellitus in relation to plasma concentrations of amino acids and acylcarnitines: a nested case-control study. Diabetes Res Clin Pract 2018; 140: 183– 190. doi: 10.1016/j.diabres.2018.03.058. 29626588 · DOI
  45. [49]Li G Gao W Xu Y Xie M Tang S Yin P. Serum metabonomics study of pregnant women with gestational diabetes mellitus based on LC-MS. Saudi J Biol Sci 2019; 26: 2057– 2063. doi: 10.1016/j.sjbs.2019.09.016. 31889794 PMC6923470 · DOI
  46. [50]Zhao H Li H Chung ACK Xiang L Li X Zheng Y. Large-scale longitudinal metabolomics study reveals different trimester-specific alterations of metabolites in relation to gestational diabetes mellitus. J Proteome Res 2019; 18: 292– 300. doi: 10.1021/acs.jproteome.8b00602. 30488697 · DOI
  47. [51]Hou W Meng X Zhao A Zhao W Pan J Tang J. Development of multimarker diagnostic models from metabolomics analysis for gestational diabetes mellitus (GDM). Mol Cell Proteomics 2018; 17: 431– 441. doi: 10.1074/mcp.RA117.000121. 29282297 PMC5836369 · DOI
  48. [52]Liu T Li J Xu F Wang M Ding S Xu H. Comprehensive analysis of serum metabolites in gestational diabetes mellitus by UPLC/Q-TOF-MS. Anal Bioanal Chem 2016; 408: 1125– 1135. doi: 10.1007/s00216-015-9211-3. 26677023 · DOI
  49. [53]Hajduk J Klupczynska A Derezinski P Matysiak J Kokot P Nowak DM. A combined metabolomic and proteomic analysis of gestational diabetes mellitus. Int J Mol Sci 2015; 16: 30034– 30045. doi: 10.3390/ijms161226133. 26694367 PMC4691080 · DOI
  50. [54]Dudzik D Zorawski M Skotnicki M Zarzycki W Kozlowska G Bibik-Malinowska K. Metabolic fingerprint of gestational diabetes mellitus. J Proteomics 2014; 103: 57– 71. doi: 10.1016/j.jprot.2014.03.025. 24698665 · DOI
  51. [55]Gao J Xu B Zhang X Cui Y Deng L Shi Z. Association between serum bile acid profiles and gestational diabetes mellitus: a targeted metabolomics study. Clin Chim Acta 2016; 459: 63– 72. doi: 10.1016/j.cca.2016.05.026. 27246871 · DOI
  52. [56]Mokkala K Vahlberg T Houttu N Koivuniemi E Laitinen K. Distinct metabolomic profile because of gestational diabetes and its treatment mode in women with overweight and obesity. Obesity (Silver Spring) 2020; 28: 1637– 1644. doi: 10.1002/oby.22882. 32705820 · DOI
  53. [57]Jiang R Wu S Fang C Wang C Yang Y Liu C. Amino acids levels in early pregnancy predict subsequent gestational diabetes. J Diabetes 2020; 12: 503– 511. doi: 10.1111/1753-0407.13018. 31883199 · DOI
  54. [58]Walejko JM Chelliah A Keller-Wood M Wasserfall C Atkinson M Gregg A. Diabetes leads to alterations in normal metabolic transitions of pregnancy as revealed by time-course metabolomics. Metabolites 2020; 10: 350 doi: 10.3390/metabo10090350. 32867274 PMC7570364 · DOI
  55. [59]Burzynska-Pedziwiatr I Jankowski A Kowalski K Sendys P Zieleniak A Cypryk K. Associations of arginine with gestational diabetes mellitus in a follow-up study. Int J Mol Sci 2020; 21: 7811 doi: 10.3390/ijms21217811. 33105558 PMC7659483 · DOI
  56. [60]Sakurai K Eguchi A Watanabe M Yamamoto M Ishikawa K Mori C. Exploration of predictive metabolic factors for gestational diabetes mellitus in Japanese women using metabolomic analysis. J Diabetes Investig 2019; 10: 513– 520. doi: 10.1111/jdi.12887. PMC6400174 29956893 · DOI
  57. [61]Pappa KI Vlachos G Theodora M Roubelaki M Angelidou K Antsaklis A. Intermediate metabolism in association with the amino acid profile during the third trimester of normal pregnancy and diet-controlled gestational diabetes. Am J Obstet Gynecol 2007; 196: e1– e5. doi: 10.1016/j.ajog.2006.06.094. 17240238 · DOI
  58. [62]She P Van Horn C Reid T Hutson SM Cooney RN Lynch CJ. Obesity-related elevations in plasma leucine are associated with alterations in enzymes involved in branched-chain amino acid metabolism. Am J Physiol Endocrinol Metab 2007; 293: E1552– E1563. doi: 10.1152/ajpendo.00134.2007. 17925455 PMC2767201 · DOI
  59. [63]Dani C Bresci C Berti E Ottanelli S Mello G Mecacci F. Metabolomic profile of term infants of gestational diabetic mothers. J Matern Fetal Neonatal Med 2014; 27: 537– 542. doi: 10.3109/14767058.2013.823941. 23844701 · DOI
  60. [64]Lu J Gu Y Liu H Wang L Li W Li W. Daily branched-chain amino acid intake and risks of obesity and insulin resistance in children: a cross-sectional study. Obesity (Silver Spring) 2020; 28: 1310– 1316. doi: 10.1002/oby.22834. 32510827 PMC7311291 · DOI
  61. [65]Sandler V Reisetter AC Bain JR Muehlbauer MJ Nodzenski M Stevens RD. Associations of maternal BMI and insulin resistance with the maternal metabolome and newborn outcomes. Diabetologia 2017; 60: 518– 530. doi: 10.1007/s00125-016-4182-2. 27981358 PMC5300897 · DOI
  62. [66]O’Connell TM. The complex role of branched chain amino acids in diabetes and cancer. Metabolites 2013; 3: 931– 945. doi: 10.3390/metabo3040931. 24958258 PMC3937834 · DOI
  63. [67]Lynch CJ Adams SH. Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev Endocrinol 2014; 10: 723– 736. doi: 10.1038/nrendo.2014.171. 25287287 PMC4424797 · DOI · Paper page
  64. [68]Um SH D’Alessio D Thomas G. Nutrient overload, insulin resistance, and ribosomal protein S6 kinase 1, S6K1. Cell Metab 2006; 3: 393– 402. doi: 10.1016/j.cmet.2006.05.003. 16753575 · DOI · Paper page
  65. [69]Nishitani S Takehana K Fujitani S Sonaka I. Branched-chain amino acids improve glucose metabolism in rats with liver cirrhosis. Am J Physiol Gastrointest Liver Physiol 2005; 288: G1292– G1300. doi: 10.1152/ajpgi.00510.2003. 15591158 · DOI
  66. [70]Gonzalez FJ Jiang C Patterson AD. An intestinal microbiota-farnesoid X receptor axis modulates metabolic disease. Gastroenterology 2016; 151: 845– 859. doi: 10.1053/j.gastro.2016.08.057. 27639801 PMC5159222 · DOI
  67. [71]Sun L Pang Y Wang X Wu Q Liu H Liu B. Ablation of gut microbiota alleviates obesity-induced hepatic steatosis and glucose intolerance by modulating bile acid metabolism in hamsters. Acta Pharm Sin B 2019; 9: 702– 710. doi: 10.1016/j.apsb.2019.02.004. 31384531 PMC6664038 · DOI
  68. [72]Jiang C Xie C Li F Zhang L Nichols RG Krausz KW. Intestinal farnesoid X receptor signaling promotes nonalcoholic fatty liver disease. J Clin Invest 2015; 125: 386– 402. doi: 10.1172/JCI76738. 25500885 PMC4382255 · DOI · Paper page
  69. [73]Xie C Jiang C Shi J Gao X Sun D Sun L. An intestinal farnesoid X receptor-ceramide signaling axis modulates hepatic gluconeogenesis in mice. Diabetes 2017; 66: 613– 626. doi: 10.2337/db16-0663. 28223344 PMC5319721 · DOI
  70. [74]Law KP Han TL Mao X Zhang H. Tryptophan and purine metabolites are consistently upregulated in the urinary metabolome of patients diagnosed with gestational diabetes mellitus throughout pregnancy: a longitudinal metabolomics study of Chinese pregnant women part 2. Clin Chim Acta 2017; 468: 126– 139. doi: 10.1016/j.cca.2017.02.018. 28238935 · DOI
  71. [75]López-Hernández Y Herrera-Van Oostdam AS Toro-Ortiz JC López JA Salgado-Bustamante M Murgu M. Urinary metabolites altered during the third trimester in pregnancies complicated by gestational diabetes mellitus: relationship with potential upcoming metabolic disorders. Int J Mol Sci 2019; 20: 1186 doi: 10.3390/ijms20051186. 30857174 PMC6429483 · DOI
  72. [76]Koos BJ Gornbein JA. Early pregnancy metabolites predict gestational diabetes mellitus: implications for fetal programming. Am J Obstet Gynecol 2020; 224: 215.e1– 215.e7. doi: 10.1016/j.ajog.2020.07.050. 32739399 · DOI
  73. [77]Lorenzo MP Dudzik D Varas E Gibellini M Skotnicki M Zorawski M. Optimization and validation of a chiral GC-MS method for the determination of free D-amino acids ratio in human urine: application to a gestational diabetes mellitus study. J Pharm Biomed Anal 2015; 107: 480– 487. doi: 10.1016/j.jpba.2015.01.015. 25679092 · DOI
  74. [78]Sulek K Han TL Villas-Boas SG Wishart DS Soh SE Kwek K. Hair metabolomics: identification of fetal compromise provides proof of concept for biomarker discovery. Theranostics 2014; 4: 953– 959. doi: 10.7150/thno.9265. 25057319 PMC4107295 · DOI
  75. [79]Chen X de Seymour JV Han TL Xia Y Chen C Zhang T. Metabolomic biomarkers and novel dietary factors associated with gestational diabetes in China. Metabolomics 2018; 14: 149 doi: 10.1007/s11306-018-1445-6. 30830425 · DOI
  76. [80]He X de Seymour JV Sulek K Qi H Zhang H Han TL. Maternal hair metabolome analysis identifies a potential marker of lipid peroxidation in gestational diabetes mellitus. Acta Diabetol 2016; 53: 119– 122. doi: 10.1007/s00592-015-0737-9. 25904507 · DOI
  77. [81]Goyal A Gupta Y Kalaivani M Sankar MJ Kachhawa G Bhatla N. Long term ((1year) postpartum glucose tolerance status among Indian women with history of gestational diabetes mellitus (GDM) diagnosed by IADPSG criteria. Diabetes Res Clin Pract 2018; 142: 154– 161. doi: 10.1016/j.diabres.2018.05.027. 29802954 · DOI
  78. [83]Allalou A Nalla A Prentice KJ Liu Y Zhang M Dai FF. A predictive metabolic signature for the transition from gestational diabetes mellitus to type 2 diabetes. Diabetes 2016; 65: 2529– 2539. doi: 10.2337/db15-1720. 27338739 PMC5001181 · DOI
  79. [84]Lai M Liu Y Ronnett GV Wu A Cox BJ Dai FF. Amino acid and lipid metabolism in post-gestational diabetes and progression to type 2 diabetes: a metabolic profiling study. PLoS Med 2020; 17: e1003112 doi: 10.1371/journal.pmed.1003112. 32433647 PMC7239388 · DOI
  80. [85]Andersson-Hall U Gustavsson C Pedersen A Malmodin D Joelsson L Holmang A. Higher concentrations of BCAAs and 3-HIB are associated with insulin resistance in the transition from gestational diabetes to type 2 diabetes. J Diabetes Res 2018; 2018: 4207067 doi: 10.1155/2018/4207067. 29967793 PMC6008749 · DOI
  81. [86]Dudzik D Zorawski M Skotnicki M Zarzycki W García A Angulo S. GC-MS based gestational diabetes mellitus longitudinal study: identification of 2-and 3-hydroxybutyrate as potential prognostic biomarkers. J Pharm Biomed Anal 2017; 144: 90– 98. doi: 10.1016/j.jpba.2017.02.056. 28314466 · DOI
  82. [87]Vangipurapu J Fernandes Silva L Kuulasmaa T Smith U Laakso M. Microbiota-related metabolites and the risk of type 2 diabetes. Diabetes Care 2020; 43: 1319– 1325. doi: 10.2337/dc19-2533. 32295805 · DOI
  83. [88]Grant SFA. The TCF7L2 locus: a genetic window into the pathogenesis of type 1 and type 2 diabetes. Diabetes Care 2019; 42: 1624– 1629. doi: 10.2337/dci19-0001. 31409726 PMC6702598 · DOI
  84. [89]Pappa KI Gazouli M Economou K Daskalakis G Anastasiou E Anagnou NP. Gestational diabetes mellitus shares polymorphisms of genes associated with insulin resistance and type 2 diabetes in the Greek population. Gynecol Endocrinol 2011; 27: 267– 272. doi: 10.3109/09513590.2010.490609. 20540670 · DOI
  85. [90]Huopio H Cederberg H Vangipurapu J Hakkarainen H Pääkkönen M Kuulasmaa T. Association of risk variants for type 2 diabetes and hyperglycemia with gestational diabetes. Eur J Endocrinol 2013; 169: 291– 297. doi: 10.1530/EJE-13-0286. 23761423 · DOI
  86. [91]Franzago M Fraticelli F Marchetti D Celentano C Liberati M Stuppia L. Nutrigenetic variants and cardio-metabolic risk in women with or without gestational diabetes. Diabetes Res Clin Pract 2018; 137: 64– 71. doi: 10.1016/j.diabres.2018.01.001. 29325775 · DOI
  87. [92]Ding M Chavarro J Olsen S Lin Y Ley SH Bao W. Genetic variants of gestational diabetes mellitus: a study of 112 SNPs among 8722 women in two independent populations. Diabetologia 2018; 61: 1758– 1768. doi: 10.1007/s00125-018-4637-8. 29947923 PMC6701842 · DOI
  88. [94]Franzago M Fraticelli F Nicolucci A Celentano C Liberati M Stuppia L. Molecular analysis of a genetic variants panel related to nutrients and metabolism: association with susceptibility to gestational diabetes and cardiometabolic risk in affected women. J Diabetes Res 2017; 2017: 4612623 doi: 10.1155/2017/4612623. 28133617 PMC5241477 · DOI
  89. [95]Papadopoulou A Lynch KF Shaat N Håkansson R Ivarsson SA Berntorp K. Gestational diabetes mellitus is associated with TCF7L2 gene polymorphisms independent of HLA-DQB1∗0602 genotypes and islet cell autoantibodies. Diabet Med 2011; 28: 1018– 1027. doi: 10.1111/j.1464-5491.2011.03359.x. 21672010 PMC3170100 · DOI
  90. [96]Potasso L Perakakis N Lamprinou A Polyzou E Kassanos D Peter A. Clinical impact of the TCF7L2 gene rs7903146 type 2 diabetes mellitus risk polymorphism in women with gestational diabetes mellitus: impaired glycemic control and increased need of insulin therapy. Exp Clin Endocrinol Diabetes 2020; 128: 663– 666. doi: 10.1055/a-1008-9223. 31546272 · DOI
  91. [97]Gaulton KJ Nammo T Pasquali L Simon JM Giresi PG Fogarty MP. A map of open chromatin in human pancreatic islets. Nat Genet 2010; 42: 255– 259. doi: 10.1038/ng.530. 20118932 PMC2828505 · DOI
  92. [98]Lyssenko V Lupi R Marchetti P Del Guerra S Orho-Melander M Almgren P. Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 2155– 2163. doi: 10.1172/JCI30706. 17671651 PMC1934596 · DOI
  93. [99]Pilgaard K Jensen CB Schou JH Lyssenko V Wegner L Brøns C. The T allele of rs7903146 TCF7L2 is associated with impaired insulinotropic action of incretin hormones, reduced 24 h profiles of plasma insulin and glucagon, and increased hepatic glucose production in young healthy men. Diabetologia 2009; 52: 1298– 1307. doi: 10.1007/s00125-009-1307-x. 19288077 · DOI
  94. [100]Cropano C Santoro N Groop L Dalla Man C Cobelli C Galderisi A. The rs7903146 variant in the TCF7L2 gene increases the risk of prediabetes/type 2 diabetes in obese adolescents by impairing beta-cell function and hepatic insulin sensitivity. Diabetes Care 2017; 40: 1082– 1089. doi: 10.2337/dc17-0290. 28611053 PMC5521977 · DOI
  95. [101]Pagán A Sabater-Molina M Olza J Prieto-Sánchez MT Blanco-Carnero JE Parrilla JJ. A gene variant in the transcription factor 7-like 2 (TCF7L2) is associated with an increased risk of gestational diabetes mellitus. Eur J Obstet Gynecol Reprod Biol 2014; 180: 77– 82. doi: 10.1016/j.ejogrb.2014.06.024. 25048152 · DOI
  96. [103]Song JY Wang HJ Ma J Xu ZY Hinney A Hebebrand J. Association of the rs10830963 polymorphism in MTNR1B with fasting glucose levels in Chinese children and adolescents. Obes Facts 2011; 4: 197– 203. doi: 10.1159/000329306. 21701235 PMC6444495 · DOI
  97. [104]Langenberg C Pascoe L Mari A Tura A Laakso M Frayling TM. Common genetic variation in the melatonin receptor 1B gene (MTNR1B) is associated with decreased early-phase insulin response. Diabetologia 2009; 52: 1537– 1542. doi: 10.1007/s00125-009-1392-x. 19455304 PMC2709880 · DOI
  98. [106]Bedoya FJ Matschinsky FM Shimizu T O’Neil JJ Appel MC. Differential regulation of glucokinase activity in pancreatic islets and liver of the rat. J Biol Chem 1986; 261: 10760– 10764. doi: 10.1016/S0021-9258(18)67451-4. 3015939 · DOI
  99. [107]Matschinsky FM. Banting Lecture 1995. A lesson in metabolic regulation inspired by the glucokinase glucose sensor paradigm. Diabetes 1996; 45: 223– 241. doi: 10.2337/diab.45.2.223. 8549869 · DOI
  100. [108]Gaulton KJ Ferreira T Lee Y Raimondo A Mägi R Reschen ME. Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci. Nat Genet 2015; 47: 1415– 1425. doi: 10.1038/ng.3437. 26551672 PMC4666734 · DOI
  101. [109]Fernandez-Mejia C Vega-Allende J Rojas-Ochoa A Rodriguez-Dorantes M Romero-Navarro G Matschinsky FM. Cyclic adenosine 3′,5′-monophosphate increases pancreatic glucokinase activity and gene expression. Endocrinology 2001; 142: 1448– 1452. doi: 10.1210/endo.142.4.8100. 11250924 · DOI
  102. [110]Grotenfelt NE Wasenius NS Rönö K Laivuori H Stach-Lempinen B Orho-Melander M. Interaction between rs10830963 polymorphism in MTNR1B and lifestyle intervention on occurrence of gestational diabetes. Diabetologia 2016; 59: 1655– 1658. doi: 10.1007/s00125-016-3989-1. 27209463 · DOI
  103. [111]Firneisz G Rosta K Al-Aissa Z Hadarits O Harreiter J Nádasdi Á. The MTNR1B rs10830963 variant in interaction with pre-pregnancy BMI is a pharmacogenetic marker for the initiation of antenatal insulin therapy in gestational diabetes mellitus. Int J Mol Sci 2018; 19: 3734 doi: 10.3390/ijms19123734. 30477160 PMC6321391 · DOI
  104. [112]Yamauchi T Kamon J Waki H Terauchi Y Kubota N Hara K. The fat-derived hormone adiponectin reverses insulin resistance associated with both lipoatrophy and obesity. Nat Med 2001; 7: 941– 946. doi: 10.1038/90984. 11479627 · DOI · Paper page
  105. [113]Achari AE Jain SK. Adiponectin, a therapeutic target for obesity, diabetes, and endothelial dysfunction. Int J Mol Sci 2017; 18: 1321 doi: 10.3390/ijms18061321. 28635626 PMC5486142 · DOI
  106. [114]Pawlik A Teler J Maciejewska A Sawczuk M Safranow K Dziedziejko V. Adiponectin and leptin gene polymorphisms in women with gestational diabetes mellitus. J Assist Reprod Genet 2017; 34: 511– 516. doi: 10.1007/s10815-016-0866-2. 28050671 PMC5401690 · DOI
  107. [115]Beltcheva O Boyadzhieva M Angelova O Mitev V Kaneva R Atanasova I. The rs266729 single-nucleotide polymorphism in the adiponectin gene shows association with gestational diabetes. Arch Gynecol Obstet 2014; 289: 743– 748. doi: 10.1007/s00404-013-3029-z. 24068295 · DOI
  108. [116]Feng Y Jiang CD Chang AM Shi Y Gao J Zhu L. Interactions among insulin resistance, inflammation factors, obesity-related gene polymorphisms, environmental risk factors, and diet in the development of gestational diabetes mellitus. J Matern Fetal Neonatal Med 2019; 32: 339– 347. doi: 10.1080/14767058.2018.1446207. 29519182 · DOI
  109. [117]Han Y Zheng YL Fan YP Liu MH Lu XY Tao Q. Association of adiponectin gene polymorphism 45TG with gestational diabetes mellitus diagnosed on the new IADPSG criteria, plasma adiponectin levels and adverse pregnancy outcomes. Clin Exp Med 2015; 15: 47– 53. doi: 10.1007/s10238-014-0275-8. 24664457 · DOI
  110. [118]Low CF Mohd Tohit ER Chong PP Idris F. Adiponectin SNP45TG is associated with gestational diabetes mellitus. Arch Gynecol Obstet 2011; 283: 1255– 1260. doi: 10.1007/s00404-010-1548-4. 20552210 · DOI
  111. [119]Takhshid MA Haem Z Aboualizadeh F. The association of circulating adiponectin and + 45 T/G polymorphism of adiponectin gene with gestational diabetes mellitus in Iranian population. J Diabetes Metab Disord 2015; 14: 30 doi: 10.1186/s40200-015-0156-z. 25909078 PMC4407396 · DOI
  112. [120]Nakatani K Noma K Nishioka J Kasai Y Morioka K Katsuki A. Adiponectin gene variation associates with the increasing risk of type 2 diabetes in non-diabetic Japanese subjects. Int J Mol Med 2005; 15: 173– 177. 15583845
  113. [121]Matharoo K Arora P Bhanwer AJ. Association of adiponectin (AdipoQ) and sulphonylurea receptor (ABCC8) gene polymorphisms with Type 2 Diabetes in North Indian population of Punjab. Gene 2013; 527: 228– 234. doi: 10.1016/j.gene.2013.05.075. 23764562 · DOI
  114. [122]Choe EY Wang HJ Kwon O Kim KJ Kim BS Lee BW. Variants of the adiponectin gene and diabetic microvascular complications in patients with type 2 diabetes. Metabolism 2013; 62: 677– 685. doi: 10.1016/j.metabol.2012.11.005. 23260797 · DOI
  115. [123]Dziedziejko V Safranow K Tarnowski M Pawlik A. Common type 2 diabetes genetic risk variants improve the prediction of gestational diabetes. Horm Metab Res 2019; 51: 655– 660. doi: 10.1055/a-0945-0328. 31299695 · DOI
  116. [124]Kasuga Y Hata K Tajima A Ochiai D Saisho Y Matsumoto T. Association of common polymorphisms with gestational diabetes mellitus in Japanese women: a case-control study. Endocr J 2017; 64: 463– 475. doi: 10.1507/endocrj.EJ16-0431. 28202837 · DOI
  117. [126]Nisa H Qi KHT Leng J Zhou T Liu H Li W. The circadian rhythm-related MTNR1B genotype, gestational weight gain, and postpartum glycemic changes. J Clin Endocrinol Metab 2018; 103: 2284– 2290. doi: 10.1210/jc.2018-00071. 29590381 PMC6276711 · DOI
  118. [127]de Carvalho AM Shao P Liu H Cheng HL Zheng Y Leng J. The MC4R genotype is associated with postpartum weight reduction and glycemic changes among women with prior gestational diabetes: longitudinal analysis. Sci Rep 2017; 7: 9654 doi: 10.1038/s41598-017-10101-x. 28852042 PMC5575005 · DOI
  119. [128]Kwak SH Choi SH Kim K Jung HS Cho YM Lim S. Prediction of type 2 diabetes in women with a history of gestational diabetes using a genetic risk score. Diabetologia 2013; 56: 2556– 2563. doi: 10.1007/s00125-013-3059-x. 24057154 · DOI · Paper page
  120. [129]Cormier H Vigneault J Garneau V Tchernof A Vohl MC Weisnagel SJ. An explained variance-based genetic risk score associated with gestational diabetes antecedent and with progression to pre-diabetes and type 2 diabetes: a cohort study. BJOG 2015; 122: 411– 419. doi: 10.1111/1471-0528.12937. 25041170 · DOI
  121. [130]Herrera-Van Oostdam AS Toro-Ortíz JC López JA Noyola DE García-López DA Durán-Figueroa NV. Placental exosomes isolated from urine of patients with gestational diabetes exhibit a differential profile expression of microRNAs across gestation. Int J Mol Med 2020; 46: 546– 560. doi: 10.3892/ijmm.2020.4626. 32626972 PMC7307810 · DOI
  122. [131]Cao YL Jia YJ Xing BH Shi DD Dong XJ. Plasma microRNA-16-5p, -17-5p and -20a-5p: novel diagnostic biomarkers for gestational diabetes mellitus. J Obstet Gynaecol Res 2017; 43: 974– 981. doi: 10.1111/jog.13317. 28621051 · DOI
  123. [132]Zhu Y Tian F Li H Zhou Y Lu J Ge Q. Profiling maternal plasma microRNA expression in early pregnancy to predict gestational diabetes mellitus. Int J Gynaecol Obstet 2015; 130: 49– 53. doi: 10.1016/j.ijgo.2015.01.010. 25887942 · DOI
  124. [133]Wang F Zhang X Zhou H. Role of cell free microRNA-19a and microRNA-19b in gestational diabetes mellitus patients. 3 Biotech 2019; 9: 406 doi: 10.1007/s13205-019-1952-9. PMC6803606 31687318 · DOI
  125. [134]Tagoma A Alnek K Kirss A Uibo R Haller-Kikkatalo K. MicroRNA profiling of second trimester maternal plasma shows upregulation of miR-195-5p in patients with gestational diabetes. Gene 2018; 672: 137– 142. doi: 10.1016/j.gene.2018.06.004. 29879500 · DOI
  126. [135]Floris I Descamps B Vardeu A Mitic T Posadino AM Shantikumar S. Gestational diabetes mellitus impairs fetal endothelial cell functions through a mechanism involving microRNA-101 and histone methyltransferase enhancer of zester homolog-2. Arterioscler Thromb Vasc Biol 2015; 35: 664– 674. doi: 10.1161/ATVBAHA.114.304730. 25614281 · DOI
  127. [136]Peng HY Li HP Li MQ. High glucose induces dysfunction of human umbilical vein endothelial cells by upregulating miR-137 in gestational diabetes mellitus. Microvasc Res 2018; 118: 90– 100. doi: 10.1016/j.mvr.2018.03.002. 29505767 · DOI
  128. [137]Lamadrid-Romero M Solís KH Cruz-Reséndiz MS Pérez JE Díaz NF Flores-Herrera H. Central nervous system development-related microRNAs levels increase in the serum of gestational diabetic women during the first trimester of pregnancy. Neurosci Res 2018; 130: 8– 22. doi: 10.1016/j.neures.2017.08.003. 28803788 · DOI
  129. [138]Liao X Zhou Z Zhang X. Effects of miR1955p on cell proliferation and apoptosis in gestational diabetes mellitus via targeting EZH2. Mol Med Rep 2020; 22: 803– 809. doi: 10.3892/mmr.2020.11142. 32626980 PMC7339727 · DOI
  130. [139]Wang J Pan Y Dai F Wang F Qiu H Huang X. Serum miR-195-5p is upregulated in gestational diabetes mellitus. J Clin Lab Anal 2020; 34: e23325 doi: 10.1002/jcla.23325. 32301163 PMC7439337 · DOI
  131. [140]Tang L Li P Li L. Whole transcriptome expression profiles in placenta samples from women with gestational diabetes mellitus. J Diabetes Investig 2020; 11: 1307– 1317. doi: 10.1111/jdi.13250. PMC7477506 32174045 · DOI
  132. [141]Yoffe L Polsky A Gilam A Raff C Mecacci F Ognibene A. Early diagnosis of gestational diabetes mellitus using circulating microRNAs. Eur J Endocrinol 2019; 181: 565– 577. doi: 10.1530/EJE-19-0206. 31539877 · DOI
  133. [142]Abdeltawab A Zaki ME Abdeldayem Y Mohamed AA Zaied SM. Circulating micro RNA-223 and angiopoietin-like protein 8 as biomarkers of gestational diabetes mellitus. Br J Biomed Sci 2021; 78: 12– 17. doi: 10.1080/09674845.2020.1764211. 32421465 · DOI
  134. [143]Martínez-Ibarra A Martínez-Razo LD Vázquez-Martínez ER Martínez-Cruz N Flores-Ramírez R García-Gómez E. Unhealthy levels of phthalates and bisphenol a in Mexican pregnant women with gestational diabetes and its association to altered expression of mirnas involved with metabolic disease. Int J Mol Sci 2019; 20: 3343 doi: 10.3390/ijms20133343. 31284700 PMC6650872 · DOI
  135. [144]Pfeiffer S Sánchez-Lechuga B Donovan P Halang L Prehn JHM Campos-Caro A. Circulating miR-330-3p in late pregnancy is associated with pregnancy outcomes among lean women with GDM. Sci Rep 2020; 10: 908 doi: 10.1038/s41598-020-57838-6. 31969632 PMC6976655 · DOI
  136. [145]Gillet V Ouellet A Stepanov Y Rodosthenous RS Croft EK Brennan K. miRNA profiles in extracellular vesicles from serum early in pregnancies complicated by gestational diabetes mellitus. J Clin Endocrinol Metab 2019; 104: 5157– 5169. doi: 10.1210/jc.2018-02693. 31058973 PMC6760296 · DOI
  137. [146]Wang P Wang H Li C Zhang X Xiu X Teng P. Dysregulation of microRNA-657 influences inflammatory response via targeting interleukin-37 in gestational diabetes mellitus. J Cell Physiol 2019; 234: 7141– 7148. doi: 10.1002/jcp.27468. 30362558 · DOI
  138. [147]Wang P Wang Z Liu G Jin C Zhang Q Man S. miR-657 promotes macrophage polarization toward M1 by targeting FAM46C in gestational diabetes mellitus. Mediators Inflamm 2019; 2019: 4851214 doi: 10.1155/2019/4851214. 31915414 PMC6930733 · DOI
  139. [148]Fulci V Scappucci G Sebastiani GD Giannitti C Franceschini D Meloni F. miR-223 is overexpressed in T-lymphocytes of patients affected by rheumatoid arthritis. Hum Immunol 2010; 71: 206– 211. doi: 10.1016/j.humimm.2009.11.008. 19931339 · DOI
  140. [149]Schaefer JS Montufar-Solis D Vigneswaran N Klein JR. Selective upregulation of microRNA expression in peripheral blood leukocytes in IL-10-/- mice precedes expression in the colon. J Immunol 2011; 187: 5834– 5841. doi: 10.4049/jimmunol.1100922. 22043014 PMC3221883 · DOI
  141. [150]Xu J Wu C Che X Wang L Yu D Zhang T. Circulating microRNAs, miR-21, miR-122, and miR-223, in patients with hepatocellular carcinoma or chronic hepatitis. Mol Carcinog 2011; 50: 136– 142. doi: 10.1002/mc.20712. 21229610 · DOI · Paper page
  142. [151]Wang X Ye L Hou W Zhou Y Wang YJ Metzger DS. Cellular microRNA expression correlates with susceptibility of monocytes/macrophages to HIV-1 infection. Blood 2009; 113: 671– 674. doi: 10.1182/blood-2008-09-175000. 19015395 PMC2628373 · DOI
  143. [152]Li X Zhang Y Zhang H Liu X Gong T Li M. miRNA-223 promotes gastric cancer invasion and metastasis by targeting tumor suppressor EPB41L3. Mol Cancer Res 2011; 9: 824– 833. doi: 10.1158/1541-7786.MCR-10-0529. 21628394 · DOI
  144. [153]Li S Li Z Guo F Qin X Liu B Lei Z. miR-223 regulates migration and invasion by targeting Artemin in human esophageal carcinoma. J Biomed Sci 2011; 18: 24 doi: 10.1186/1423-0127-18-24. 21453483 PMC3080798 · DOI
  145. [154]Zampetaki A Kiechl S Drozdov I Willeit P Mayr U Prokopi M. Plasma microRNA profiling reveals loss of endothelial miR-126 and other microRNAs in type 2 diabetes. Circ Res 2010; 107: 810– 817. doi: 10.1161/CIRCRESAHA.110.226357. 20651284 · DOI
  146. [155]Lu H Buchan RJ Cook SA. MicroRNA-223 regulates Glut4 expression and cardiomyocyte glucose metabolism. Cardiovasc Res 2010; 86: 410– 420. doi: 10.1093/cvr/cvq010. 20080987 · DOI
  147. [156]Haneklaus M Gerlic M O’Neill LAJ. Masters SL. miR-223: infection, inflammation and cancer. J Intern Med 2013; 274: 215– 226. doi: 10.1111/joim.12099. 23772809 PMC7166861 · DOI
  148. [157]Zhao C Dong J Jiang T Shi Z Yu B Zhu Y. Early second-trimester serum miRNA profiling predicts gestational diabetes mellitus. PLoS One 2011; 6: e23925 doi: 10.1371/journal.pone.0023925. 21887347 PMC3161072 · DOI · Paper page
  149. [158]Deng L Huang Y Li L Chen H Su J. Serum miR-29a/b expression in gestational diabetes mellitus and its influence on prognosis evaluation. J Int Med Res 2020; 48: 300060520954763 doi: 10.1177/0300060520954763. 32993411 PMC7536503 · DOI
  150. [159]Tung CW Ho C Hsu YC Huang SC Shih YH Lin CL. MicroRNA-29a attenuates diabetic glomerular injury through modulating cannabinoid receptor 1 signaling. Molecules 2019; 24: 264 doi: 10.3390/molecules24020264. 30642005 PMC6359641 · DOI
  151. [160]Zhang YL Chen XQ. Dysregulation of microRNA-770-5p influences pancreatic-beta-cell function by targeting TP53 regulated inhibitor of apoptosis 1 in gestational diabetes mellitus. Eur Rev Med Pharmacol Sci 2020; 24: 793– 801. doi: 10.26355/eurrev_202001_20062. 32016984 · DOI
  152. [161]Li L Wang S Li H Wan J Zhou Q Zhou Y. microRNA-96 protects pancreatic beta-cell function by targeting PAK1 in gestational diabetes mellitus. Biofactors 2018; 44: 539– 547. doi: 10.1002/biof.1461. 30536654 · DOI
  153. [162]Sun DG Tian S Zhang L Hu Y Guan CY Ma X. The miRNA-29b is downregulated in placenta during gestational diabetes mellitus and may alter placenta development by regulating trophoblast migration and invasion through a HIF3A-dependent mechanism. Front Endocrinol (Lausanne) 2020; 11: 169 doi: 10.3389/fendo.2020.00169. 32296392 PMC7137738 · DOI
  154. [163]Zhou X Xiang C. Zheng X. miR-132 serves as a diagnostic biomarker in gestational diabetes mellitus and its regulatory effect on trophoblast cell viability. Diagn Pathol 2019; 14: 119 doi: 10.1186/s13000-019-0899-9. 31653266 PMC6814988 · DOI
  155. [164]Zhao C Zhang T Shi Z Ding H Ling X. MicroRNA-518d regulates PPARalpha protein expression in the placentas of females with gestational diabetes mellitus. Mol Med Rep 2014; 9: 2085– 2090. doi: 10.3892/mmr.2014.2058. 24639097 · DOI
  156. [165]Hromadnikova I Kotlabova K Dvorakova L Krofta L. Diabetes mellitus and cardiovascular risk assessment in mothers with a history of gestational diabetes mellitus based on postpartal expression profile of micrornas associated with diabetes mellitus and cardiovascular and cerebrovascular diseases. Int J Mol Sci 2020; 21: 2437 doi: 10.3390/ijms21072437. 32244558 PMC7177375 · DOI
  157. [166]Lee IR Dawson SA Wetherall JD Hahnel R. Sex hormone-binding globulin secretion by human hepatocarcinoma cells is increased by both estrogens and androgens. J Clin Endocrinol Metab 1987; 64: 825– 831. doi: 10.1210/jcem-64-4-825. 3029159 · DOI
  158. [167]Maged AM Moety GA Mostafa WA Hamed DA. Comparative study between different biomarkers for early prediction of gestational diabetes mellitus. J Matern Fetal Neonatal Med 2014; 27: 1108– 1112. doi: 10.3109/14767058.2013.850489. 24090161 · DOI
  159. [168]Zhang T Du T Li W Yang S Liang W. Sex hormone-binding globulin levels during the first trimester may predict gestational diabetes mellitus development. Biomark Med 2018; 12: 239– 244. doi: 10.2217/bmm-2016-0030. 29460646 · DOI
  160. [169]Hedderson MM Xu F Darbinian JA Quesenberry CP Sridhar S Kim C. Prepregnancy SHBG concentrations and risk for subsequently developing gestational diabetes mellitus. Diabetes Care 2014; 37: 1296– 1303. doi: 10.2337/dc13-1965. 24561392 PMC3994937 · DOI
  161. [170]Haffner SM Katz MS Stern MP Dunn JF. The relationship of sex hormones to hyperinsulinemia and hyperglycemia. Metabolism 1988; 37: 683– 688. doi: 10.1016/0026-0495(88)90091-1. 3290626 · DOI · Paper page
  162. [171]Plymate SR Matej LA Jones RE Friedl KE. Inhibition of sex hormone-binding globulin production in the human hepatoma (Hep G2) cell line by insulin and prolactin. J Clin Endocrinol Metab 1988; 67: 460– 464. doi: 10.1210/jcem-67-3-460. 2842359 · DOI · Paper page
  163. [173]Yasui T Tomita J Miyatani Y Yamada M Uemura H Irahara M. Associations of adiponectin with sex hormone-binding globulin levels in aging male and female populations. Clin Chim Acta 2007; 386: 69– 75. doi: 10.1016/j.cca.2007.08.001. 17825805 · DOI
  164. [174]Akin F Bastemir M Alkis E. Effect of insulin sensitivity on SHBG levels in premenopausal versus postmenopausal obese women. Adv Ther 2007; 24: 1210– 1220. doi: 10.1007/BF02877767. 18165203 · DOI
  165. [175]Wang X Chi X Feng C Zhang X Jin Z. Sex hormone-binding globulin regulates the activity of the ERK pathway in the placentas of patients with gestational diabetes mellitus. Biochem Biophys Res Commun 2020; 532: 613– 619. doi: 10.1016/j.bbrc.2020.08.100. 32900481 · DOI
  166. [176]Bost F Aouadi M Caron L Even P Belmonte N Prot M. The extracellular signal-regulated kinase isoform ERK1 is specifically required for in vitro and in vivo adipogenesis. Diabetes 2005; 54: 402– 411. doi: 10.2337/diabetes.54.2.402. 15677498 · DOI
  167. [177]Jager J Gremeaux T Cormont M Le Marchand-Brustel Y Tanti JF. Interleukin-1beta-induced insulin resistance in adipocytes through down-regulation of insulin receptor substrate-1 expression. Endocrinology 2007; 148: 241– 251. doi: 10.1210/en.2006-0692. 17038556 PMC1971114 · DOI
  168. [178]Chi X Feng C Wang X Jin Z. Sex hormone-binding globulin regulates glucose metabolism in human placental trophoblasts via cAMP/PKA/CREB1. J Obstet Gynaecol Res 2020; 46: 2340– 2346. doi: 10.1111/jog.14429. 32830408 PMC7692910 · DOI
  169. [179]Shen L Zhao D Chen Y Zhang K Chen X Lin J. Comparative proteomics analysis of serum proteins in gestational diabetes during early and middle stages of pregnancy. Proteomics Clin Appl 2019; 13: e1800060 doi: 10.1002/prca.201800060. 31162828 · DOI
  170. [180]Mavreli D Evangelinakis N Papantoniou N Kolialexi A. Quantitative comparative proteomics reveals candidate biomarkers for the early prediction of gestational diabetes mellitus: a preliminary study. In Vivo 2020; 34: 517– 525. doi: 10.21873/invivo.11803. 32111749 PMC7157887 · DOI
  171. [181]Zhao D Shen L Wei Y Xie J Chen S Liang Y. Identification of candidate biomarkers for the prediction of gestational diabetes mellitus in the early stages of pregnancy using iTRAQ quantitative proteomics. Proteomics Clin Appl 2017; 11 doi: 10.1002/prca.201600152. 28220636 · DOI
  172. [182]Claesson R Ignell C Shaat N Berntorp K. HbA1c as a predictor of diabetes after gestational diabetes mellitus. Prim Care Diabetes 2017; 11: 46– 51. doi: 10.1016/j.pcd.2016.09.002. 27692850 · DOI
  173. [183]Kwon SS Kwon JY Park YW Kim YH Lim JB. HbA1c for diagnosis and prognosis of gestational diabetes mellitus. Diabetes Res Clin Pract 2015; 110: 38– 43. doi: 10.1016/j.diabres.2015.07.014. 26344325 · DOI
  174. [184]Coetzee A Mason D Hall DR Hoffmann M Conradie M. Evidence for the utility of antenatal HbA1c to predict early postpartum diabetes after gestational diabetes in South Africa. Diabetes Res Clin Pract 2018; 143: 50– 55. doi: 10.1016/j.diabres.2018.06.021. 29969724 · DOI
  175. [185]Lappas M Jinks D Shub A Willcox JC Georgiou HM Permezel M. Postpartum IGF-I and IGFBP-2 levels are prospectively associated with the development of type 2 diabetes in women with previous gestational diabetes mellitus. Diabetes Metab 2016; 42: 442– 447. doi: 10.1016/j.diabet.2016.06.004. 27387739 · DOI
  176. [186]Lappas M Georgiou HM Velagic A Willcox JC Permezel M Shub A. Do postpartum levels of apolipoproteins prospectively predict the development of type 2 diabetes in women with previous gestational diabetes mellitus? Exp Clin Endocrinol Diabetes 2019; 127: 353– 358. doi: 10.1055/a-0577-7700. 29529691 · DOI
  177. [188]Blatt AJ Nakamoto JM Kaufman HW. Gaps in diabetes screening during pregnancy and postpartum. Obstet Gynecol 2011; 117: 61– 68. doi: 10.1097/AOG.0b013e3181fe424b. 21173645 · DOI

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Authors
Lu, W., & Hu, C.
Affiliations
Shanghai Jiao Tong University (CN)
Corresponding author
Hu, Cheng
ORCID
Hu, Cheng
Venue
Chinese Medical Journal
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2022-08-20
Volume
135
Issue
16
Pages
1940-1951
ISSN
  • 0366-6999 (ISSN-L)
  • 2542-5641
DOI
10.1097/cm9.0000000000002160
PMC
PMC9746787
OpenAlex
W4296805594
Cited by
52
References
11
Citations by year
2026: 14 · 2025: 18 · 2024: 14 · 2023: 6
FWCI
1.91
Type
journal article
Language
en
Open access
Licence
Locations
  • Chinese Medical Journal (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
Keywords
gestational diabetes mellitus, postpartum diabetes, molecular biomarkers, metabolomics, single nucleotide polymorphisms, microRNA, proteins, diabetes screening, diabetes diagnosis, risk prediction
MeSH terms
Diabetes Mellitus, Type 2, Female, Humans, Pregnancy, Biomarkers, Diabetes, Gestational, MicroRNAs, Proteomics, Postpartum Period

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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