Diabetes Metab J.  2025 Jan;49(1):128-143. 10.4093/dmj.2024.0139.

Identification and Potential Clinical Utility of Common Genetic Variants in Gestational Diabetes among Chinese Pregnant Women

Affiliations
  • 1Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
  • 2Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China
  • 3CUHK-SJTU Joint Research Center in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China
  • 4Scientific Research Platform of the Second School of Clinical Medicine, Guangdong Medical University, Dongguan, China
  • 5Department of Obstetrics and Gynecology, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
  • 6Development and Reproduction Laboratory, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
  • 7School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong, China
  • 8Chinese University of Hong Kong-Sichuan University Joint Laboratory in Reproductive Medicine, The Chinese University of Hong Kong, Hong Kong, China
  • 9Department of Children’s Health, Tianjin Women and Children’s Health Center, Tianjin, China
  • 10Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
  • 11Department of Pediatrics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
  • 12Department of Obstetrics and Gynecology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
  • 13Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
  • 14Department of Obstetrics and Gynecology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China

Abstract

Background
The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications.
Methods
We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants.
Results
Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI],1.38 to 1.96]), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; Pmeta=7.6×10-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; Pmeta=3.1×10-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals.
Conclusion
Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Keyword

Diabetes, gestational; Genetic risk score; Genome-wide association study; Glucose intolerance; Pregnant women

Figure

  • Fig. 1. Results for meta-analysis of genome-wide association study for gestational diabetes. (A) Manhattan plot. The y-axis represents the −log10 P value (adjusted for principal components and age), and the x-axis represents the 6,322,337 analyzed biallelic single nucleotide polymorphisms. The dashed red horizontal line corresponds to the genome-wide significance threshold for P<5×10–8. There are 4 points with P<5×10–8, and the arrow and labels localize the susceptibility loci to gestational diabetes mellitus (GDM) discovered in the present study. (B) Quantile-quantile (Q-Q) plot. The dotted line corresponds to the null hypothesis. TBR1, T-box brain transcription factor 1; SLC4A10, solute carrier family 4 member 10; CDKAL1, CDK5 regulatory subunit-associated protein 1-like 1; MTNR1B, melatonin receptor 1B; INS-IGF2, insulin-insulin-like growth factor 2; KCNQ1, potassium voltage-gated channel subfamily Q member 1.

  • Fig. 2. Results for the two genome-wide significant loci for gestational diabetes. (A) Forest plot for the association between T-box brain transcription factor 1 (TBR1)-solute carrier family 4 member 10 (SLC4A10) rs117781972 and gestational diabetes mellitus (GDM) in all discovery and replication cohorts. Odds ratio (OR) and 95% confidence interval (CI) were reported according to the A-allele of rs117781972 (i.e., the GDM-associated risk allele). (B) Forest plot for the association between melatonin receptor 1B (MTNR1B) rs7945617 and GDM in all discovery and replication cohorts. ORs and 95% CIs were reported according to the C-allele of rs7945617 (i.e., the GDM-associated risk allele). A total of three studies (i.e., the Hyperglycemia and Adverse Pregnancy Outcome-Hong Kong [HAPO-HK] Study, the Tianjin Study, and the Treated GDM Cases vs. Non-diabetes Controls [TGDM-NDM] Study) were included in the “meta-analysis of discovery cohorts.” For the GENetics of Diabetes In Pregnancy (GenDIP) meta-analysis, the P values of the associations were obtained from the meta-regression implemented in Meta-Regression of Multi-AncEstry Genetic Association (MR-MEGA) and the combined OR and 95% CI was estimated by meta-analysis under a fixed effect model. (C) Regional plot of the TBR1-SLC4A10 locus. (D) Regional plot of the MTNR1B locus. The purple diamonds represent the sentinel single nucleotide polymorphisms (SNPs) rs117781972 and rs7945617 identified from the meta-analysis of genome-wide association studies. Other SNPs are colored according to their level of linkage disequilibrium, which is measured by r2, with the sentinel SNPs. The recombination rates estimated from the 1000 Genomes project Asian data are shown. The genes in the interval are indicated in the bottom panel. TANK, TRAF family member associated NFKB activator; PSMD14, proteasome 26S subunit, non-ATPase 14; AHCTF1P1, AT-hook containing transcription factor 1 pseudogene 1; FAT3, FAT atypical cadherin 3; CCDC67, coiled-coil domain containing 87.

  • Fig. 3. Forest plot for the association between potassium voltage-gated channel subfamily Q member 1 (KCNQ1) rs2237897 and gestational diabetes mellitus in all discovery and replication cohorts. Odds ratio (OR) and 95% confidence interval (CI) were reported according to the C-allele of rs2237897 (i.e., the type 2 diabetes mellitus-associated risk allele). For the GENetics of Diabetes In Pregnancy (GenDIP) meta-analysis, the P value of the association was obtained from the meta-regression implemented in Meta-Regression of Multi-AncEstry Genetic Association (MR-MEGA) and the combined OR and 95% CI was estimated by meta-analysis under a fixed effect model. A total of three studies (i.e., the Hyperglycemia and Adverse Pregnancy Outcome-Hong Kong [HAPO-HK] Study, the Tianjin Study, and the Tianjin Study and the Treated GDM Cases vs. Non-diabetes Controls [TGDM-NDM] Study) were included in the “meta-analysis of discovery cohorts.” A total of six studies (i.e., the HAPO-HK Study, the Tianjin Study, the TGDM-NDM Study, the Guangzhou Study, the FinnGen Study, and the Mexican Study) were included in the “overall meta-analysis.” We did not include the GenDIP samples in the overall meta-analysis because they overlapped with both the HAPO-HK and FinnGen Studies.

  • Fig. 4. Association between quintiles of polygenic risk score (PRS) and gestational diabetes. (A) Meta-analysis of three discovery cohorts of Chinese women (the Hyperglycemia and Adverse Pregnancy Outcome-Hong Kong [HAPO-HK] Study, Tianjin Study, and Tianjin Study and the Treated GDM Cases vs. Non-diabetes Controls [TGDM-NDM] Study). (B) Guangzhou Study. (C) HAPO-Thai Study. (D) HAPO-Hispanic Study. Plinear is the P value testing for a linear trend across the quintile categories of PRS. Ptop is the P value testing for the association of a high PRS with gestational diabetes mellitus (GDM) by comparing the top 20% with the remaining 80% of the PRS distribution. Odds ratio (OR) and 95% confidence interval (CI) of GDM were stratified by quintile categories of PRS. Within each individual cohort, P values were obtained from logistic regression with the adjustment of principal components, age and body mass index, except for the Guangzhou Study which did not adjust for any covariates. Results from the three discovery cohorts were then meta-analyzed using a fixed-effects model.

  • Fig. 5. Association between quintiles of polygenic risk score (PRS) and abnormal glucose tolerance (AGT) at 7-year postpartum in the Hyperglycemia and Adverse Pregnancy Outcome-Hong Kong (HAPO-HK) Study. (A) PRS derived based on four gestational diabetes mellitus (GDM)-related variants. (B) PRS derived based on 286 type 2 diabetes mellitus (T2DM)-related variants. The T2DM-related PRS was derived based on 286 T2DM-related variants reported by the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) consortium [20]. Plinear is the P value testing for a linear trend across the quintile categories of PRS. Ptop is the P value testing for the association of a high PRS with AGT after pregnancy by comparing the top 20% with the remaining 80% of the PRS distribution. Odds ratio (OR) and 95% confidence interval (CI) of GDM were stratified by quintile categories of PRS. P values were obtained from logistic regression with the adjustment of principal components, age and body mass index.


Reference

1. Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, et al. IDF Diabetes Atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group’s criteria. Diabetes Res Clin Pract. 2022; 183:109050.
Article
2. HAPO Study Cooperative Research Group, Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008; 358:1991–2002.
Article
3. Vounzoulaki E, Khunti K, Abner SC, Tan BK, Davies MJ, Gillies CL. Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis. BMJ. 2020; 369:m1361.
Article
4. Lowe WL Jr, Lowe LP, Kuang A, Catalano PM, Nodzenski M, Talbot O, et al. Maternal glucose levels during pregnancy and childhood adiposity in the Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study. Diabetologia. 2019; 62:598–610.
Article
5. Lowe WL Jr, Scholtens DM, Kuang A, Linder B, Lawrence JM, Lebenthal Y, et al. Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study (HAPO FUS): maternal gestational diabetes mellitus and childhood glucose metabolism. Diabetes Care. 2019; 42:372–80.
6. Tam WH, Ma RC, Ozaki R, Li AM, Chan MH, Yuen LY, et al. In utero exposure to maternal hyperglycemia increases childhood cardiometabolic risk in offspring. Diabetes Care. 2017; 40:679–86.
Article
7. Powe CE, Kwak SH. Genetic studies of gestational diabetes and glucose metabolism in pregnancy. Curr Diab Rep. 2020; 20:69.
Article
8. Pervjakova N, Moen GH, Borges MC, Ferreira T, Cook JP, Allard C, et al. Multi-ancestry genome-wide association study of gestational diabetes mellitus highlights genetic links with type 2 diabetes. Hum Mol Genet. 2022; 31:3377–91.
9. Elliott A, Walters RK, Pirinen M, Kurki M, Junna N, Goldstein JI, et al. Distinct and shared genetic architectures of gestational diabetes mellitus and type 2 diabetes. Nat Genet. 2024; 56:377–82.
Article
10. Tian Y, Li P. Genetic risk score to improve prediction and treatment in gestational diabetes mellitus. Front Endocrinol (Lausanne). 2022; 13:955821.
Article
11. Kawai VK, Levinson RT, Adefurin A, Kurnik D, Collier SP, Conway D, et al. A genetic risk score that includes common type 2 diabetes risk variants is associated with gestational diabetes. Clin Endocrinol (Oxf). 2017; 87:149–55.
Article
12. Leng J, Shao P, Zhang C, Tian H, Zhang F, Zhang S, et al. Prevalence of gestational diabetes mellitus and its risk factors in Chinese pregnant women: a prospective population-based study in Tianjin, China. PLoS One. 2015; 10:e0121029.
Article
13. Tam WH, Ma RCW, Ozaki R, Li AM, Chan MHM, Yuen LY, et al. Antenatal treatment of gestational diabetes and offspring’s future cardiometabolic risk. In : The 9th International Symposium on Diabetes, Hypertension and Metabolic Syndrome and in Pregnancy; 2017 Mar 8-12; Barcelona, Spain.
14. Ko GT, Chan JC, Chan AW, Wong PT, Hui SS, Tong SD, et al. Association between sleeping hours, working hours and obesity in Hong Kong Chinese: the ‘better health for better Hong Kong’ health promotion campaign. Int J Obes (Lond). 2007; 31:254–60.
Article
15. Wu L, Song Y, Zhang Y, Liang B, Deng Y, Tang T, et al. Novel genetic variants of PPARγ2 promoter in gestational diabetes mellitus and its molecular regulation in adipogenesis. Front Endocrinol (Lausanne). 2021; 11:499788.
Article
16. International Association of Diabetes and Pregnancy Study Groups Consensus Panel, Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010; 33:676–82.
Article
17. ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. 2. Classification and diagnosis of diabetes: standards of care in diabetes-2023. Diabetes Care. 2023; 46(Suppl 1):S19–40.
18. Kwak SH, Kim SH, Cho YM, Go MJ, Cho YS, Choi SH, et al. A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes. 2012; 61:531–41.
Article
19. Changalidis AI, Maksiutenko EM, Barbitoff YA, Tkachenko AA, Vashukova ES, Pachuliia OV, et al. Aggregation of genomewide association data from FinnGen and UK Biobank replicates multiple risk loci for pregnancy complications. Genes (Basel). 2022; 13:2255.
Article
20. Mahajan A, Spracklen CN, Zhang W, Ng MC, Petty LE, Kitajima H, et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet. 2022; 54:560–72.
21. Huerta-Chagoya A, Vazquez-Cardenas P, Moreno-Macias H, Tapia-Maruri L, Rodriguez-Guillen R, Lopez-Vite E, et al. Genetic determinants for gestational diabetes mellitus and related metabolic traits in Mexican women. PLoS One. 2015; 10:e0126408.
Article
22. Ao D, Wang HJ, Wang LF, Song JY, Yang HX, Wang Y. The rs22 37892 polymorphism in KCNQ1 influences gestational diabetes mellitus and glucose levels: a case-control study and meta-analysis. PLoS One. 2015; 10:e0128901.
23. Yasuda K, Miyake K, Horikawa Y, Hara K, Osawa H, Furuta H, et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat Genet. 2008; 40:1092–7.
Article
24. Pascoe L, Tura A, Patel SK, Ibrahim IM, Ferrannini E, Zeggini E, et al. Common variants of the novel type 2 diabetes genes CDKAL1 and HHEX/IDE are associated with decreased pancreatic beta-cell function. Diabetes. 2007; 56:3101–4.
25. Lyssenko V, Nagorny CL, Erdos MR, Wierup N, Jonsson A, Spegel P, et al. Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat Genet. 2009; 41:82–8.
Article
26. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007; 316:1336–41.
Article
27. Okamura T, Yanobu-Takanashi R, Takeuchi F, Isono M, Akiyama K, Shimizu Y, et al. Deletion of CDKAL1 affects high-fat diet-induced fat accumulation and glucose-stimulated insulin secretion in mice, indicating relevance to diabetes. PLoS One. 2012; 7:e49055.
Article
28. Wei FY, Suzuki T, Watanabe S, Kimura S, Kaitsuka T, Fujimura A, et al. Deficit of tRNA(Lys) modification by Cdkal1 causes the development of type 2 diabetes in mice. J Clin Invest. 2011; 121:3598–608.
Article
29. Shimizu I, Yoshida Y, Minamino T. A role for circadian clock in metabolic disease. Hypertens Res. 2016; 39:483–91.
Article
30. Yamagata K, Senokuchi T, Lu M, Takemoto M, Fazlul Karim M, Go C, et al. Voltage-gated K+ channel KCNQ1 regulates insulin secretion in MIN6 β-cell line. Biochem Biophys Res Commun. 2011; 407:620–5.
Article
31. Deriziotis P, O’Roak BJ, Graham SA, Estruch SB, Dimitropoulou D, Bernier RA, et al. De novo TBR1 mutations in sporadic autism disrupt protein functions. Nat Commun. 2014; 5:4954.
Article
32. den Hoed J, Sollis E, Venselaar H, Estruch SB, Deriziotis P, Fisher SE. Functional characterization of TBR1 variants in neurodevelopmental disorder. Sci Rep. 2018; 8:14279.
Article
33. Jacobs S, Ruusuvuori E, Sipila ST, Haapanen A, Damkier HH, Kurth I, et al. Mice with targeted Slc4a10 gene disruption have small brain ventricles and show reduced neuronal excitability. Proc Natl Acad Sci U S A. 2008; 105:311–6.
34. Davies G, Lam M, Harris SE, Trampush JW, Luciano M, Hill WD, et al. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nat Commun. 2018; 9:2098.
35. Trubetskoy V, Pardinas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022; 604:502–8.
36. Williams CM, Labouret G, Wolfram T, Peyre H, Ramus F. A general cognitive ability factor for the UK Biobank. Behav Genet. 2023; 53:85–100.
Article
37. Xiang AH, Wang X, Martinez MP, Walthall JC, Curry ES, Page K, et al. Association of maternal diabetes with autism in offspring. JAMA. 2015; 313:1425–34.
Article
38. Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet. 2018; 50:1412–25.
39. Sakaue S, Kanai M, Tanigawa Y, Karjalainen J, Kurki M, Koshiba S, et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat Genet. 2021; 53:1415–24.
Article
40. Huang J, Huffman JE, Huang Y, Do Valle I, Assimes TL, Raghavan S, et al. Genomics and phenomics of body mass index reveals a complex disease network. Nat Commun. 2022; 13:7973.
Article
41. Zhu Z, Guo Y, Shi H, Liu CL, Panganiban RA, Chung W, et al. Shared genetic and experimental links between obesity-related traits and asthma subtypes in UK Biobank. J Allergy Clin Immunol. 2020; 145:537–49.
Article
42. Popova PV, Klyushina AA, Vasilyeva LB, Tkachuk AS, Vasukova EA, Anopova AD, et al. Association of common genetic risk variants with gestational diabetes mellitus and their role in GDM prediction. Front Endocrinol (Lausanne). 2021; 12:628582.
Article
43. Powe CE, Nodzenski M, Talbot O, Allard C, Briggs C, Leya MV, et al. Genetic determinants of glycemic traits and the risk of gestational diabetes mellitus. Diabetes. 2018; 67:2703–9.
Article
44. Shen Y, Jia Y, Li Y, Gu X, Wan G, Zhang P, et al. Genetic determinants of gestational diabetes mellitus: a case-control study in two independent populations. Acta Diabetol. 2020; 57:843–52.
Article
45. Ding M, Chavarro J, Olsen S, Lin Y, Ley SH, Bao W, et al. Genetic variants of gestational diabetes mellitus: a study of 112 SNPs among 8722 women in two independent populations. Diabetologia. 2018; 61:1758–68.
Article
46. Lamri A, Mao S, Desai D, Gupta M, Pare G, Anand SS. Fine-tuning of genome-wide polygenic risk scores and prediction of gestational diabetes in South Asian Women. Sci Rep. 2020; 10:8941.
Article
Full Text Links
  • DMJ
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2025 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr