Welcome to visit Zhongnan Medical Journal Press Series journal website!

Exploring the causal association of immunophenotypes on gestational diabetes mellitus based on Mendelian randomization

Published on Oct. 31, 2025Total Views: 50 timesTotal Downloads: 15 timesDownloadMobile

Author: WANG Juan 1 WANG Qiong 2 LIN Xingguang 3 YE Xiaohe 1 SHEN Li 1

Affiliation: 1. Department of Obstetrics and Gynecology, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430077, China 2. Department of Obstetrics and Gynecology, Chongyang Maternal and Child Health Hospital, Xianning 437500, Hubei Province, China 3. Department of Obstetrics and Gynecology, Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China

Keywords: Gestational diabetes mellitus Immune cell phenotype Mendelian randomization Genome-wide association study Causality

DOI: 10.12173/j.issn.1004-5511.202410144

Reference: Wang J, Wang Q, Lin XG, Ye XH, Shen L. Exploring the causal association of immunophenotypes on gestational diabetes mellitus based on Mendelian randomization[J]. Yixue Xinzhi Zazhi, 2025, 35(10): 1173-1180. DOI: 10.12173/j.issn.1004-5511.202410144. [Article in Chinese]

  • Abstract
  • Full-text
  • References
Abstract

Objective  To explore the causal relationship between immune cell phenotypes and the risk of developing gestational diabetes mellitus (GDM) by using Mendelian randomization (MR).

Methods  Using genome-wide association study data from European populations, we extracted 731 immune cell traits as instrumental variables, and obtained GDM summary statistics from the Finnish database (FinnGen). The inverse-variance weighted (IVW) method served as the primary estimator, supplemented by the weighted median, MR-Egger regression, simple mode, and weighted mode to evaluate the causal relationship between immune cell phenotypes and GDM, with additional tests for heterogeneity, horizontal pleiotropy, and sensitivity analyses. Reverse MR was further conducted to assess the potential causal impact of GDM on immune phenotypes.

Results  A total of 40 immune phenotypes showed potential causal associations with GDM risk. After false discovery rate correction (FDR<0.05), two phenotypes remained significant: HLA DR on CD33- HLA DR+ [OR=1.107, 95%CI (1.050, 1.166)] and HLA DR on DC [OR=1.098, 95%CI (1.048, 1.150)]. Reverse MR revealed no significant causal impact of GDM on any of the 40 immune phenotypes (P>0.05).

Conclusion  Specific immune cell phenotypes (HLA DR on CD33- HLA DR+, HLA DR on DC) may be potential causal risk factors for the development of GDM and may provide targets for future risk prediction and immune intervention strategies.

Full-text
Please download the PDF version to read the full text: download
References

1.Choudhury AA, Devi Rajeswari V. Gestational diabetes mellitus - a metabolic and reproductive disorder[J]. Biomed Pharmacother, 2021, 143: 112183. DOI: 10.1016/j.biopha.2021.112183.

2.Guo HY, Tang SB, Li LJ, et al. Gestational diabetes mellitus causes genome hyper-methylation of oocyte via increased EZH2[J]. Nat Commun, 2025, 16(1): 127. DOI: 10.1038/s41467-024-55499-x.

3.Karcz K, Królak-Olejnik B. Impact of gestational diabetes mellitus on fetal growth and nutritional status in newborns[J]. Nutrients, 2024, 16(23): 4093. DOI: 10.3390/nu16234093.

4.Xu P, Dong S, Wu L, et al. Maternal and placental DNA methylation changes associated with the pathogenesis of gestational diabetes mellitus[J]. Nutrients, 2022, 15(1): 157. DOI: 10.3390/nu15010157.

5.Ustianowski Ł, Udzik J, Szostak J, et al. Genetic and epigenetic factors in gestational diabetes mellitus pathology[J]. Int J Mol Sci, 2023, 24(23): 16419. DOI: 10.3390/ijms242316619.

6.Ray GW, Zeng Q, Kusi P, et al. Genetic and inflammatory factors underlying gestational diabetes mellitus: a review[J]. Front Endocrinol (Lausanne), 2024, 15: 1399694. DOI: 10.3389/fendo.2024.1399694.

7.Li X, Yu D, Wang Y, et al. The intestinal dysbiosis of mothers with gestational diabetes mellitus (GDM) and its impact on the gut microbiota of their newborns[J]. Can J Infect Dis Med Microbiol, 2021, 2021: 3044534. DOI: 10.1155/2021/3044534.

8.Huang X, Wang L, Zhao S, et al. Pregnancy induces an immunological memory characterized by maternal immune alterations through specific genes methylation[J]. Front Immunol, 2021, 12: 686676. DOI: 10.3389/fimmu.2021.686676.

9.Chang RQ, Zhou WJ, Li DJ, et al. Innate lymphoid cells at the maternal-fetal interface in human pregnancy[J]. Int J Biol Sci, 2020, 16(6): 957-969. DOI: 10.7150/ijbs.38264.

10.Ying W, Fu W, Lee YS, et al. The role of macrophages in obesity-associated islet inflammation and β-cell abnormalities[J]. Nat Rev Endocrinol, 2020, 16(2): 81-90. DOI: 10.1038/s41574-019-0284-7.

11.Ji Q, Li X, Wang Y, et al. Periostin acts as a bridge between gestational diabetes mellitus (GDM) and chronic inflammation to modulate insulin resistance by modulating PPARα/NF-κB/TNF-α signaling pathway[J]. Endocr Metab Immune Disord Drug Targets, 2023, 23(13): 1649-1659. DOI: 10.2174/1871530323666230427104724.

12.Zgutka K, Tkacz M, Tomasiak P, et al. Gestational diabetes mellitus-induced inflammation in the placenta via IL-1β and toll-like receptor pathways[J]. Int J Mol Sci, 2024, 25(21): 31245. DOI: 10.3390/ijms252111409.

13.Nakamura S, Mori K, Okuma H, et al. Age-associated decline of monocyte insulin sensitivity in diabetic and healthy individuals[J]. Diab Vasc Dis Res, 2021, 18(1): 1479164121989281. DOI: 10.1177/1479164121989281.

14.Draffin CR, Alderdice FA, McCance DR, et al. Exploring the needs, concerns and knowledge of women diagnosed with gestational diabetes: a qualitative study[J]. Midwifery, 2016, 40: 141-147. DOI: 10.1016/j.midw.2016.06.019.

15.Nazarzadeh M, Pinho-Gomes AC, Bidel Z, et al. Plasma lipids and risk of aortic valve stenosis: a Mendelian randomization study[J]. Eur Heart J, 2020, 41(40): 3913-3920. DOI: 10.1093/eurheartj/ehaa070.

16.Skrivankova VW, Richmond RC, Woolf BAR, et al. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization[J]. JAMA, 2021, 326(16): 1614-1621. DOI: 10.1001/jama.2021.18236.

17.Zheng J, Baird D, Borges MC, et al. Recent developments in Mendelian randomization studies[J]. Curr Epidemiol Rep, 2017, 4(4): 330-345. DOI: 10.1007/s40471-017-0128-6.

18.Orru V, Steri M, Sidore C, et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy[J]. Nat Genet, 2020, 52(10): 1036-1045. DOI: 10.1038/s41588-020-0684-4.

19.Pierce BL, Ahsan H, VanderWeele TJ. 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.

20.Burgess S, Scott RA, Timpson NJ, et al. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors[J]. Eur J Epidemiol, 2015, 30(7): 543-552. DOI: 10.1007/s10654-015-0011-z.

21.Bowden J, Davey Smith G, Haycock PC, et al. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator[J]. Genet Epidemiol, 2016, 40(4): 304-314. DOI: 10.1002/gepi.21965.

22.Bowden J, Spiller W, Del Greco MF, et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the radial plot and radial regression[J]. Int J Epidemiol, 2018, 47(6): 2100-2110. DOI: 10.1093/ije/dyy213.

23.Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through egger regression[J]. Int J Epidemiol, 2015, 44(2): 512-525. DOI: 10.1093/ije/dyv080.

24.Verbanck M, Chen CY, Neale B, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases[J]. Nat Genet, 2018, 50(5): 693-698. DOI: 10.1038/s41588-018-0099-7.

25.Cerqueira C, Manfroi B, Fillatreau S. IL-10-producing regulatory B cells and plasmocytes: molecular mechanisms and disease relevance[J]. Semin Immunol, 2019, 44: 101323. DOI: 10.1016/j.smim.2019.101323.

26.Dutta S, Sengupta P, Haque N. Reproductive immunomodulatory functions of B cells in pregnancy[J]. Int Rev Immunol, 2020, 39(2): 53-66. DOI: 10.1080/08830185.2019.1674299.

27.Schumacher A, Costa SD, Zenclussen AC. Endocrine factors modulating immune responses in pregnancy[J]. Frontiers in Immunology, 2014, 5: 196. DOI: 10.3389/fimmu.2014.00196.

28.Peng Y, Yu H, Yang J, et al. Altered Treg and Th17 cell responses in patients with gestational diabetes mellitus[J]. Gynecol Endocrinol, 2019, 35(8): 703-707. DOI: 10.1080/09513590.2019.1597343.

29.Barbosa P, Pinho A, Lázaro A, et al. Treg cells play a role in the obesity-associated insulin resistance [J]. Life Sci, 2024, 336: 122306. DOI: 10.1016/j.lfs.2023.122306.

30.Zhou T, Hu Z, Yang S, et al. Role of adaptive and innate immunity in type 2 diabetes mellitus[J]. J Diabetes Res, 2018, 2018: 7457269. DOI: 10.1155/2018/7457269.

31.Hotamisligil GS. Inflammation and metabolic disorders[J]. Nature, 2006, 444(7121): 860-867. DOI: 10.1038/nature05485.

32.Souwer Y, Chamuleau ME, van de Loosdrecht AA, et al. Detection of aberrant transcription of major histocompatibility complex class II antigen presentation genes in chronic lymphocytic leukaemia identifies HLA-DOA mRNA as a prognostic factor for survival[J]. Br J Haematol, 2009, 145(3): 334-343. DOI: 10.1111/j.1365-2141.2009.07695.x.

33.Macy AM, Herrmann LM, Adams AC, et al. Major histocompatibility complex class II in the tumor microenvironment: functions of nonprofessional antigen-presenting cells[J]. Curr Opin Immunol, 2023, 83: 102330. DOI: 10.1016/j.coi.2023.102330.

34.Kust SA, Ustiuzhanina MO, Streltsova MA, et al. HLA-DR expression in natural killer cells marks distinct functional states, depending on cell differentiation stage[J]. Int J Mol Sci, 2024, 25(9): 6551. DOI: 10.3390/ijms25094609.

35.Yang SW, Cho EH, Choi SY, et al. DC-SIGN expression in Hofbauer cells may play an important role in immune tolerance in fetal chorionic villi during the development of preeclampsia[J]. J Reprod Immunol, 2017, 124: 30-37. DOI: 10.1016/j.jri.2017. 09.012.

36.Wojcik M, Zielenia K, Zurawska-Klis M, et al. Increased expression of immune-related genes in leukocytes of patients with diagnosed gestational diabetes mellitus (GDM) [J]. Exp Biol Med (Maywood), 2016, 241(5): 457-465. DOI: 10.1177/1535370215615699.

37.Wu L, Yan Z, Jiang Y, et al. Metabolic regulation of dendritic cell activation and immune function during inflammation[J]. Front Immunol, 2023, 14: 1140749. DOI: 10.3389/fimmu.2023. 1140749.

38.康静, 薄运兰, 李滔, 等. 中国妊娠期糖尿病孕妇不良妊娠结局危险因素的Meta分析[J]. 数理医药学杂志, 2024, 37(11): 847-851. [Kang J, Bo YL, Li T, et al. Risk factors of adverse pregnancy outcomes in pregnant women with gestational diabetes mellitus in China: a Meta-analysis[J]. Journal of Mathematical Medicine, 2024, 37(11): 847-851.] DOI: 10.12173/j.issn.1004-4337.202407152.

39.Ji Z, Zhang C, Yuan J, et al. Is there a causal association between gestational diabetes mellitus and immune mediators? A bidirectional Mendelian randomization analysis[J]. Front Endocrinol, 2024, 15: 1358144. DOI: 10.3389/fendo.2024.1358144.