Objective To investigate the causal association among glycemic traits, plasma metabolites, and adverse pregnancy outcomes (APOs), as well as potential mediating mechanisms, using Mendelian randomization (MR).
Methods Based on summary data from genome-wide association studies, a two-sample MR framework, including sensitivity analyses, reverse MR, and two-step mediation analysis was applied. Four glycemic traits (fasting glucose, fasting insulin, 2 h postprandial glucose, and HbA1c) and 1,400 plasma metabolites were used as exposures, while four APOs (spontaneous abortion, fetal growth restriction, pre-eclampsia/eclampsia, and preterm birth) served as outcomes. The inverse variance weighted method was used as the primary analysis, supplemented by various sensitivity analyses to verify the robustness of the results.
Results MR analysis showed that genetically predicted higher fasting glucose was causally associated with a reduced risk of fetal growth restriction [OR=0.660, 95%CI (0.496, 0.879), P=0.004]. Higher fasting insulin was a risk factor for pre-eclampsia/eclampsia [OR=1.738, 95%CI (1.057, 2.859), P=0.030], while higher 2 h postprandial glucose was associated with an increased risk of preterm birth [OR=1.172, 95%CI (1.014, 1.354), P=0.032]. No significant causal associations were found between HbA1c and any of the APOs in the MR analysis (P > 0.05). Metabolite analysis revealed that higher levels of hexanoylglutamine and 3-methylcytidine increased the risk of pre-eclampsia/eclampsia, whereas a higher level of N6-acetyllysine reduced the risk of preterm birth. Mediation analysis further suggested that 3-methylcytidine may partially mediate the effect of fasting insulin on pre-eclampsia/eclampsia (mediation proportion: 4.88%). All primary results were validated by sensitivity analyses, and reverse MR analysis found no evidence of reverse causation.
Conclusion This study provides genetic evidence for the potential differential causal effects of glycemic traits on distinct APOs and, reveals the partial mediating role of 3-methylcytidine in the pathway from fasting insulin to pre-eclampsia/eclampsia, offering new insights for precise glycemic management during pregnancy.
1. ZelekeAM, TakeleGA, GoneteYA, et al. Adverse birth outcomes and associated factors among Sub-Saharan African grand multiparas: a systematic review and Meta-analysis[J]. Ther Adv Reprod Health, 2025, 19: 26334941251342121. doi:10.1177/26334941251342121
2. SheikhJ, AlloteyJ, KewT, et al. Effects of race and ethnicity on perinatal outcomes in high-income and upper-middle-income countries: an individual participant data Meta-analysis of 2 198 655 pregnancies[J]. Lancet, 2022, 400(10368): 2049-2062.
3. ZengN, WenW, CorsiDJ, et al. Maternal glucose levels and future risk of developing cardiovascular disease: a systematic review and Meta-analysis protocol[J]. BMJ Open, 2023, 13(5): e069251. doi:10.1136/bmjopen-2022-069251
4. SalmanL, ArbibN, ShmueliA, et al. The association between pre-pregnancy impaired fasting glucose and adverse perinatal outcome[J].Diabetes Res Clin Pract, 2018, 140: 148-153. doi:10.1016/j.diabres.2018.03.038
5. YeW, LuoC, HuangJ, et al. Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and Meta-analysis[J]. BMJ, 2022, 377: e067946. doi:10.1136/bmj-2021-067946
6. PingaultJB, O'ReillyPF, SchoelerT, et al. Using genetic data to strengthen causal inference in observational research[J]. Nat Rev Genet, 2018, 19(9): 566-580. doi:10.1038/s41576-018-0020-3
7. ZhenJ, GuY, WangP, et al. Genome-wide association and Mendelian randomisation analysis among 30,699 Chinese pregnant women identifies novel genetic and molecular risk factors for gestational diabetes and glycaemic traits[J]. Diabetologia, 2024, 67(4): 703-713. doi:10.1007/s00125-023-06065-5
8. ChenH, ShaoLZ, WangYX, et al. Causal relationships between leukocyte subsets and adverse fetal outcomes: a Mendelian randomization study[J]. Mediators Inflamm, 2024, 2024: 6349687. doi:10.1155/mi/6349687
9. ArdissinoM, ReddyRK, SlobEAW, et al. Maternal hypertensive traits and adverse outcome in pregnancy: a Mendelian randomization study[J]. J Hypertens, 2023, 41(9): 1438-1445. doi:10.1097/hjh.0000000000003486
10. YangXF, ZhongQM, HuangMW, et al. Causal relationship between gestational diabetes and preeclampsia: a bidirectional Mendelian randomization analysis[J]. Diabetes Res Clin Pract, 2024, 210: 111643. doi:10.1016/j.diabres.2024.111707
11. HantoushzadehS, Zaki-DizajiM, HabibiD, et al. Pregestational diabetes and adverse pregnancy results: a Mendelian randomization study[J]. Arch Iran Med, 2025, 28(2): 81-87. doi:10.34172/aim.33461
12. ChenJ, SpracklenCN, MarenneG, et al. The trans-ancestral genomic architecture of glycemic traits[J]. Nat Genet, 2021, 53(6): 840-860.
13. KurkiMI, KarjalainenJ, PaltaP, et al. FinnGen provides genetic insights from a well-phenotyped isolated population[J]. Nature, 2023, 613(7944): 508-518.
14. ChenY, LuT, Pettersson-KymmerU, et al. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases[J]. Nat Genet, 2023, 55(1): 44-53. doi:10.1038/s41588-022-01270-1
15. SandersonE, GlymourMM, HolmesMV, et al. Mendelian randomization[J]. Nat Rev Methods Primers, 2022, 2: 6. doi:10.1038/s43586-021-00092-5
16. PierceBL, AhsanH, VanderweeleTJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants[J]. Int J Epidemiol, 2011, 40(3): 740-752. doi:10.1093/ije/dyq151
17. WongTHT, MoJMY, ZhouM, et al. A two-sample Mendelian randomization study explores metabolic profiling of different glycemic traits[J]. Commun Biol, 2024, 7(1): 293. doi:10.1038/s42003-024-05977-1
18. 闫浩杰, 史树锦, 韩帆, 等. 循环炎性细胞因子与冠状动脉粥样硬化的因果关系:双向孟德尔随机化研究[J]. 解放军医学杂志, 2026, 51(2): 155-163.YanHJ, ShiSJ, HanF, et al. Causal relationship between circulating inflammatory cytokines and coronary atherosclerosis:a bidirectional Mendelian randomization study[J]. Medical Journal of Chinese People's Liberation Army, 2026, 51(2): 155-163.
19. ChenJ, YuX, WuX, et al. Causal relationships between gut microbiota, immune cell, and non-small cell lung cancer: a two-step, two-sample Mendelian randomization study[J]. J Cancer, 2024, 15(7): 1890-1897. doi:10.7150/jca.92699
20. MitranoviciMI, ChioreanDM, MoraruR, et al. Understanding the pathophysiology of preeclampsia: exploring the role of antiphospholipid antibodies and future directions[J]. J Clin Med, 2024, 13(9): 2668. doi:10.3390/jcm13092668
21. HauthJC, CliftonRG, RobertsJM, et al. Maternal insulin resistance and preeclampsia[J]. Am J Obstet Gynecol, 2011, 204(4): 327.e1-6. doi:10.1016/j.ajog.2011.02.024
22. MasouraS, MakedouK, TheodoridisT, et al. The involvement of uric acid in the pathogenesis of preeclampsia[J]. Curr Hypertens Rev, 2015, 11(2): 110-115. doi:10.2174/1573402111666150529130703
23. GuptaS, AgarwalA, SharmaRK. The role of placental oxidative stress and lipid peroxidation in preeclampsia[J]. Obstet Gynecol Surv, 2005, 60(12): 807-816. doi:10.1097/01.ogx.0000193879.79268.59
24. YingX, WuQ, LiX, et al. Causal associations between pre-pregnancy diabetes mellitus and pre-eclampsia risk: insights from a Mendelian randomization study[J]. Healthcare(Basel), 2025, 13(9): 1085. doi:10.3390/healthcare13091085
25. HuXQ, ZhangL. Mitochondrial dysfunction in the pathogenesis of preeclampsia[J]. Curr Hypertens Rep, 2022, 24(6): 157-172. doi:10.1007/s11906-022-01184-7
26. BarthaJL, VisiedoF, Fernández-DeuderoA, et al. Decreased mitochondrial fatty acid oxidation in placentas from women with preeclampsia[J]. Placenta, 2012, 33(2): 132-134. doi:10.1016/j.placenta.2011.11.027
27. GuY, ChuX, MorganJA, et al. Upregulation of METTL3 expression and m6A RNA methylation in placental trophoblasts in preeclampsia[J]. Placenta, 2021, 103: 43-49. doi:10.1016/j.placenta.2020.10.016
28. UllahA, ZhaoJ, SinglaRK, et al. Pathophysiological impact of CXC and CX3CL1 chemokines in preeclampsia and gestational diabetes mellitus[J]. Front Cell Dev Biol, 2023, 11: 1272536. doi:10.3389/fcell.2023.1272536
29. KarczK, Królak-OlejnikB. Impact of gestational diabetes mellitus on fetal growth and nutritional status in newborns[J]. Nutrients, 2024, 16(23): 4093. doi:10.3390/nu16234093
30. BertossaMR, DarbyJRT, HolmanSL, et al. Fetal glucose availability: a key regulator of the metabolic, hormonal and contractility profiles of the fetal sheep heart[J/OL]. J Physiol, 1-23. [2025-09-01]. https://physoc.onlinelibrary.wiley.com/doi/10.1113/JP288303. doi:10.1113/jp288303
31. LoweWL, KuangA, HayesMG, et al. Genetics of glucose homeostasis in pregnancy and postpartum[J]. Diabetologia, 2024, 67(12): 2726-2739. doi:10.1007/s00125-024-06256-8
32. GuoF, LiuY, DingZ, et al. Observations of the effects of maternal fasting plasma glucose changes in early pregnancy on fetal growth profiles and birth outcomes[J]. Front Endocrinol (Lausanne), 2021, 12: 666194. doi:10.3389/fendo.2021.666194
33. LiuB, ChenH, XuY, et al. Fetal growth is associated with maternal fasting plasma glucose at first prenatal visit[J]. PLoS One, 2014, 9(12): e116352. doi:10.1371/journal.pone.0116352