Skip to main content

Advertisement

SpringerLink
Log in

FTO, GCKR, CDKAL1 and CDKN2A/B gene polymorphisms and the risk of gestational diabetes mellitus: a meta-analysis

  • Maternal-Fetal Medicine
  • Published:
Archives of Gynecology and Obstetrics Aims and scope Submit manuscript

Abstract

Purpose

Studies had examined the associations between genetic polymorphisms and the risk of gestational diabetes mellitus (GDM). However, conclusions of these studies were controversial due to the smaller sample size and limited statistical power. We carried out a meta-analysis with the aim of providing a more comprehensive summary of the currently available research to evaluate the relationship between FTO, GCKR, CDKAL1 and CDKN2A/B gene polymorphisms and GDM risk.

Methods

Literature search was carried out in the PubMed, EMBASE, Web of Science, China National Knowledge Infrastructure and Wangfang databases up to November 2017. Data were extracted by two independent reviewers and statistical analyses were performed with STATA software. Pooled odds ratios and 95% confidence intervals were calculated by Z test to assess the association between genetic polymorphisms and GDM risk. Stratified analysis was performed based on ethnicity. Heterogeneity and publication bias between studies were evaluated by Cochran’s Q test and Egger regression test, respectively.

Results

14 eligible studies were included. CDKAL1 rs7754840 and rs7756992 showed significant correlation with GDM risk under the allele, recessive, dominant, homozygote and heterozygote models. GCKR rs780094 and CDKN2A/B rs10811661 also showed the same association under the allele, recessive and heterozygote models. No associations between FTO rs9939609 and rs8050136, GCKR rs1260326 and GDM risk were found.

Conclusions

Our meta-analysis showed that two SNPs in particular(rs7754840 and rs7756992 in CDKAL1) were very strongly associated with GDM risk. GCKR rs780094 and CDKN2A/B rs10811661 polymorphisms were moderately associated with GDM risk.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Reece EA, Leguizamon G, Wiznitzer A (2009) Gestational diabetes: the need for a common ground. Lancet 373(9677):1789–1797

    Article  PubMed  Google Scholar 

  2. Hadden DR (1985) Geographic, ethnic, and racial variations in the incidence of gestational diabetes mellitus. Diabetes 34(Suppl 2):8–12

    Article  PubMed  Google Scholar 

  3. Shaat N, Groop L (2007) Genetics of gestational diabetes mellitus. Curr Med Chem 14(5):569–583

    Article  CAS  PubMed  Google Scholar 

  4. Bellamy L, Casas JP, Hingorani AD, Williams D (2009) Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet 373(9677):1773–1779

    Article  CAS  PubMed  Google Scholar 

  5. Lauenborg J, Grarup N, Damm P, Borch-Johnsen K, Jorgensen T, Pedersen O, Hansen T (2009) Common type 2 diabetes risk gene variants associate with gestational diabetes. J Clin Endocrinol Metab 94(1):145–150

    Article  CAS  PubMed  Google Scholar 

  6. Kasuga Y, Hata K, Tajima A, Ochiai D, Saisho Y, Matsumoto T, Arata N, Miyakoshi K, Tanaka M (2017) Association of common polymorphisms with gestational diabetes mellitus in Japanese women: a case–control study. Endocr J 64(4):463–475

    Article  PubMed  Google Scholar 

  7. Shaat N, Karlsson E, Lernmark A, Ivarsson S, Lynch K, Parikh H, Almgren P, Berntorp K, Groop L (2006) Common variants in MODY genes increase the risk of gestational diabetes mellitus. Diabetologia 49(7):1545–1551

    Article  CAS  PubMed  Google Scholar 

  8. Tarnowski M, Malinowski D, Pawlak K, Dziedziejko V, Safranow K, Pawlik A (2017) GCK, GCKR, FADS1, DGKB/TMEM195 and CDKAL1 gene polymorphisms in women with gestational diabetes. Can J Diabetes 41(4):372–379

    Article  PubMed  Google Scholar 

  9. Tarnowski M, Malinowski D, Safranow K, Dziedziejko V, Pawlik A (2017) CDC123/CAMK1D gene rs12779790 polymorphism and rs10811661 polymorphism upstream of the CDKN2A/2B gene in women with gestational diabetes. J Perinatol 37(4):345–348

    Article  CAS  PubMed  Google Scholar 

  10. Chang S, Wang Z, Wu L, Lu X, Shangguan S, Xin Y, Li L, Wang L (2017) Association between TCF7L2 polymorphisms and gestational diabetes mellitus: a meta-analysis. J Diabetes Investig 8(4):560–570

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wang L, Xu W, Wang X (2016) Peroxisome proliferator-activated receptor Pro12Ala polymorphism and the risks of gestational diabetes mellitus: an updated meta-analysis of 12 studies. Med (Baltimore) 95(44):e5090

    Article  CAS  Google Scholar 

  12. Liu Q, Huang Z, Li H, Bai J, Liu X, Ye H (2016) Relationship between melatonin receptor 1B (rs10830963 and rs1387153) with gestational diabetes mellitus: a case-control study and meta-analysis. Arch Gynecol Obstet 294(1):55–61

    Article  CAS  PubMed  Google Scholar 

  13. Ao D, Wang HJ, Wang LF, Song JY, Yang HX, Wang Y (2015) The rs2237892 polymorphism in KCNQ1 influences gestational diabetes mellitus and glucose levels: a case–control study and meta-analysis. PLoS One 10(6):e0128901

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Han X, Cui H, Chen X, Xie W, Chang Y (2015) Association of the glucokinase gene promoter polymorphism -30G > A (rs1799884) with gestational diabetes mellitus susceptibility: a case–control study and meta-analysis. Arch Gynecol Obstet 292(2):291–298

    Article  CAS  PubMed  Google Scholar 

  15. Mao H, Li Q, Gao S (2012) Meta-analysis of the relationship between common type 2 diabetes risk gene variants with gestational diabetes mellitus. PLoS One 7(9):e45882

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang C, Bao W, Rong Y, Yang H, Bowers K, Yeung E, Kiely M (2013) Genetic variants and the risk of gestational diabetes mellitus: a systematic review. Hum Reprod Update 19(4):376–390

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN, Doney AS, Morris AD, Smith GD, Hattersley AT, McCarthy MI (2007) A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316(5826):889–894

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS, McCarthy MI, Hattersley AT (2007) Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 316(5829):1336–1341

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Horikoshi M, Hara K, Ito C, Shojima N, Nagai R, Ueki K, Froguel P, Kadowaki T (2007) Variations in the HHEX gene are associated with increased risk of type 2 diabetes in the Japanese population. Diabetologia 50(12):2461–2466

    Article  CAS  PubMed  Google Scholar 

  20. Omori S, Tanaka Y, Takahashi A, Hirose H, Kashiwagi A, Kaku K, Kawamori R, Nakamura Y, Maeda S (2008) Association of CDKAL1, IGF2BP2, CDKN2A/B, HHEX, SLC30A8, and KCNJ11 with susceptibility to type 2 diabetes in a Japanese population. Diabetes 57(3):791–795

    Article  CAS  PubMed  Google Scholar 

  21. Cho YM, Kim TH, Lim S, Choi SH, Shin HD, Lee HK, Park KS, Jang HC (2009) Type 2 diabetes-associated genetic variants discovered in the recent genome-wide association studies are related to gestational diabetes mellitus in the Korean population. Diabetologia 52(2):253–261

    Article  CAS  PubMed  Google Scholar 

  22. Franzago M, Fraticelli F, Nicolucci A, Celentano C, Liberati M, Stuppia L, Vitacolonna E (2017) 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 2017:4612623

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Rose CS, Ek J, Urhammer SA, Glumer C, Borch-Johnsen K, Jorgensen T, Pedersen O, Hansen T (2005) A −30G > A polymorphism of the beta-cell-specific glucokinase promoter associates with hyperglycemia in the general population of whites. Diabetes 54(10):3026–3031

    Article  CAS  PubMed  Google Scholar 

  24. Mohas M, Kisfali P, Jaromi L, Maasz A, Feher E, Csongei V, Polgar N, Safrany E, Cseh J, Sumegi K, Hetyesy K, Wittmann I, Melegh B (2010) GCKR gene functional variants in type 2 diabetes and metabolic syndrome: do the rare variants associate with increased carotid intima-media thickness? Cardiovasc Diabetol 9:79

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Stuebe AM, Wise A, Nguyen T, Herring A, North KE, Siega-Riz AM (2014) Maternal genotype and gestational diabetes. Am J Perinatol 31(1):69–76

    PubMed  Google Scholar 

  26. Ubeda M, Rukstalis JM, Habener JF (2006) Inhibition of cyclin-dependent kinase 5 activity protects pancreatic beta cells from glucotoxicity. J Biol Chem 281(39):28858–28864

    Article  CAS  PubMed  Google Scholar 

  27. Ching YP, Pang AS, Lam WH, Qi RZ, Wang JH (2002) Identification of a neuronal Cdk5 activator-binding protein as Cdk5 inhibitor. J Biol Chem 277(18):15237–15240

    Article  CAS  PubMed  Google Scholar 

  28. Steinthorsdottir V, Thorleifsson G, Reynisdottir I, Benediktsson R, Jonsdottir T, Walters GB, Styrkarsdottir U, Gretarsdottir S, Emilsson V, Ghosh S, Baker A, Snorradottir S, Bjarnason H, Ng MC, Hansen T, Bagger Y, Wilensky RL, Reilly MP, Adeyemo A, Chen Y, Zhou J, Gudnason V, Chen G, Huang H, Lashley K, Doumatey A, So WY, Ma RC, Andersen G, Borch-Johnsen K, Jorgensen T, van Vliet-Ostaptchouk JV, Hofker MH, Wijmenga C, Christiansen C, Rader DJ, Rotimi C, Gurney M, Chan JC, Pedersen O, Sigurdsson G, Gulcher JR, Thorsteinsdottir U, Kong A, Stefansson K (2007) A variant in CDKAL1 influences insulin response and risk of type 2 diabetes. Nat Genet 39(6):770–775

    Article  CAS  PubMed  Google Scholar 

  29. Pascoe L, Tura A, Patel SK, Ibrahim IM, Ferrannini E, Zeggini E, Weedon MN, Mari A, Hattersley AT, McCarthy MI, Frayling TM, Walker M (2007) Common variants of the novel type 2 diabetes genes CDKAL1 and HHEX/IDE are associated with decreased pancreatic beta-cell function. Diabetes 56(12):3101–3104

    Article  CAS  PubMed  Google Scholar 

  30. Krishnamurthy J, Ramsey MR, Ligon KL, Torrice C, Koh A, Bonner-Weir S, Sharpless NE (2006) p16INK4a induces an age-dependent decline in islet regenerative potential. Nature 443(7110):453–457

    Article  CAS  PubMed  Google Scholar 

  31. Rane SG, Dubus P, Mettus RV, Galbreath EJ, Boden G, Reddy EP, Barbacid M (1999) Loss of Cdk4 expression causes insulin-deficient diabetes and Cdk4 activation results in beta-islet cell hyperplasia. Nat Genet 22(1):44–52

    Article  CAS  PubMed  Google Scholar 

  32. Wei F, Cai C, Feng S, Lv J, Li S, Chang B, Zhang H, Shi W, Han H, Ling C, Yu P, Chen Y, Sun N, Tian J, Jiao H, Yang F, Li M, Wang Y, Zou L, Su L, Li J, Li R, Qiu H, Shi J, Liu S, Chang M, Lin J, Chen L, Li WD (2015) TOX and CDKN2A/B gene polymorphisms are associated with type 2 diabetes in Han Chinese. Sci Rep 5:11900

    Article  PubMed  PubMed Central  Google Scholar 

  33. Stang A (2010) Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 25(9):603–605

    Article  PubMed  Google Scholar 

  34. Bauer T, Bouman HJ, van Werkum JW, Ford NF, ten Berg JM, Taubert D (2011) Impact of CYP2C19 variant genotypes on clinical efficacy of antiplatelet treatment with clopidogrel: systematic review and meta-analysis. BMJ 343:d4588

    Article  PubMed  PubMed Central  Google Scholar 

  35. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7(3):177–188

    Article  CAS  PubMed  Google Scholar 

  36. Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558

    Article  PubMed  Google Scholar 

  37. Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315(7109):629–634

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Aris NKM, Ismai NAM, Mahdy ZA, Ahmad S, Naim NM, Siraj HHH, Jaafar R, Ishak S, Harun R, Jamal R (2011) An analysis of targeted single nucleotide polymorphisms for the risk prediction of gestational diabetes mellitus in a cohort of Malaysian patients. Asia-Pacific J Mol Med 1(1):1

    Google Scholar 

  39. Wu Y, Li S, Zhang Z (2015) Association between gene polymorphism of CDKAL1 and gestational diabetes mellitus. Chin J Diabetes 23(6):501–504

    CAS  Google Scholar 

  40. Wang Y, Nie M, Li W, Ping F, Hu Y, Ma L, Gao J, Liu J (2011) Association of six single nucleotide polymorphisms with gestational diabetes mellitus in a chinese population. PLoS One 6(11):e26953

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Pagan A, Sabater-Molina M, Olza J, Prieto-Sanchez MT, Blanco-Carnero JE, Parrilla JJ, Gil A, Larque E (2014) 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 180:77–82

    Article  CAS  PubMed  Google Scholar 

  42. de Melo SF, Frigeri HR, dos Santos-Weiss IC, Rea RR, de Souza EM, Alberton D, de Moraes Gomes, Rego F, Picheth G (2015) Polymorphisms in FTO and TCF7L2 genes of Euro-Brazilian women with gestational diabetes. Clin Biochem 48(16–17):1064–1067

    Article  CAS  PubMed  Google Scholar 

  43. Kanthimathi S, Chidambaram M, Liju S, Bhavadharini B, Bodhini D, Prakash VG, Amutha A, Bhavatharini A, Anjana RM, Mohan V, Radha V (2015) Identification of genetic variants of gestational diabetes in South Indians. Diabetes Technol Therapeutics 17(7):462–467

    Article  CAS  Google Scholar 

  44. Anghebem-Oliveira MI, Webber S, Alberton D, de Souza EM, Klassen G, Picheth G, Rego FG (2017) The GCKR gene polymorphism rs780094 is a risk factor for gestational diabetes in a Brazilian Population. J Clin Lab Anal 31(2):e22035

    Article  CAS  Google Scholar 

  45. Saucedo R, Valencia J, Gutierrez C, Basurto L, Hernandez M, Puello E, Rico G, Vega G, Zarate A (2017) Gene variants in the FTO gene are associated with adiponectin and TNF-alpha levels in gestational diabetes mellitus. Diabetol Metab Syndr 9(1):32

    Article  PubMed  PubMed Central  Google Scholar 

  46. Huopio H, Cederberg H, Vangipurapu J, Hakkarainen H, Paakkonen M, Kuulasmaa T, Heinonen S, Laakso M (2013) Association of risk variants for type 2 diabetes and hyperglycemia with gestational diabetes. Eur J Endocrinol 169(3):291–297

    Article  CAS  PubMed  Google Scholar 

  47. Kwak SH, Kim SH, Cho YM, Go MJ, Cho YS, Choi SH, Moon MK, Jung HS, Shin HD, Kang HM, Cho NH, Lee IK, Kim SY, Han BG, Jang HC, Park KS (2012) A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes 61(2):531–541

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by Changzhou science and technology support project (Social Development CE20175021). We thank Dr. Xuejiao Chen for the help of data collection and Dr. Rui Yang for the help of data analysis. We are grateful to the researchers who provided their data for these analyses and for subjects who participated in the original studies.

Author information

Author notes

  1. Fang Guo and Wei Long contribute equal to this work.

Authors and Affiliations

  1. Changzhou Maternity and Child Health Care Hospital affiliated to Nanjing Medical University, Changzhou City, Jiangsu Province, 213003, China

    Fang Guo, Wei Long, Wenbai Zhou, Bin Zhang, Jianbing Liu & Bin Yu

Authors
  1. Fang Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Long
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenbai Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Bin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianbing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bin Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Fang Guo: Protocol development, Data Collection, Manuscript writing. Wei Long: Data Collection, Data analysis. Wenbai Zhou: Data Collection. Bin Zhang: Data Collection. Jianbing Liu: Data analysis. Bin Yu: Review and revise the manuscript.

Corresponding author

Correspondence to Bin Yu.

Ethics declarations

Conflict of interest

The authors declare that we have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

404_2018_4857_MOESM1_ESM.eps

Supplementary Fig. 1 Forest plots of the relationship between FTO rs9939609 polymorphism and the risk of GDM under the recessive, dominant, homozygote contrast and heterozygote contrast models in overall populations. Legends A: recessive model (AA vs. TT+AT); B: dominant model (AT+AA vs. TT); C: homozygote contrast model (AA vs. TT); D: heterozygote contrast model (AT vs. AA) (EPS 3673 kb)

404_2018_4857_MOESM2_ESM.eps

Supplementary Fig. 2 Forest plots of the relationship between FTO rs8050136 polymorphism and the risk of GDM under the recessive, dominant, homozygote contrast and heterozygote contrast models in overall or subgroup populations. Legends A: recessive model (AA vs. CC+CA); B: dominant model (CA+AA vs. CC); C: homozygote contrast model (AA vs. CC); D: heterozygote contrast model (CA vs. AA) (EPS 4092 kb)

404_2018_4857_MOESM3_ESM.eps

Supplementary Fig. 3 Forest plots of the relationship between GCKR rs1260326 polymorphism and the risk of GDM under the recessive, dominant, homozygote contrast and heterozygote contrast models in overall or subgroup populations. Legends A: recessive model (TT vs. CC+CT); B: dominant model (CT+TT vs. CC); C: homozygote contrast model (TT vs. CC); D: heterozygote contrast model (CT vs. TT) (EPS 1144 kb)

404_2018_4857_MOESM4_ESM.eps

Supplementary Fig. 4 Forest plots of the relationship between GCKR rs780094 polymorphism and the risk of GDM under the recessive, dominant, homozygote contrast and heterozygote contrast models in overall or subgroup populations. Legends A: recessive model (CC vs. CT+TT); B: dominant model (CC+CT vs. TT); C: homozygote contrast model (CC vs. TT); D: heterozygote contrast model (CT vs. CC) (EPS 4435 kb)

404_2018_4857_MOESM5_ESM.eps

Supplementary Fig. 5 Forest plots of the relationship between CDKAL1 rs7754840 polymorphism and the risk of GDM under the recessive, dominant, homozygote contrast and heterozygote contrast models in overall populations. Legends A: recessive model (CC vs. GG+GC); B: dominant model (GC+CC vs. GG); C: homozygote contrast model (CC vs. GG); D: heterozygote contrast model (GC vs. CC) (EPS 4461 kb)

404_2018_4857_MOESM6_ESM.eps

Supplementary Fig. 6 Forest plots of the relationship between CDKAL1 rs7756992 polymorphism and the risk of GDM under the recessive, dominant, homozygote contrast and heterozygote contrast models in overall or subgroup populations. Legends A: recessive model (GG vs. AA+GA); B: dominant model (GA+GG vs. AA); C: homozygote contrast model (GG vs. AA); D: heterozygote contrast model (GA vs. GG) (EPS 4257 kb)

404_2018_4857_MOESM7_ESM.eps

Supplementary Fig. 7 Forest plots of the relationship between CDKN2A/B rs10811661 polymorphism and the risk of GDM under the recessive, dominant, homozygote contrast and heterozygote contrast models in overall or subgroup populations. Legends A: recessive model (TT vs. TC+CC); B: dominant model (TT+TC vs. CC); C: homozygote contrast model (TT vs. CC); D: heterozygote contrast model (TC vs. TT) (EPS 3717 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, F., Long, W., Zhou, W. et al. FTO, GCKR, CDKAL1 and CDKN2A/B gene polymorphisms and the risk of gestational diabetes mellitus: a meta-analysis. Arch Gynecol Obstet 298, 705–715 (2018). https://doi.org/10.1007/s00404-018-4857-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00404-018-4857-7

Keywords

Search

Navigation