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Diabetologia
Published in: Diabetologia 8/2018

01-08-2018 | Article

Genetic variants of gestational diabetes mellitus: a study of 112 SNPs among 8722 women in two independent populations

Authors: Ming Ding, Jorge Chavarro, Sjurdur Olsen, Yuan Lin, Sylvia H. Ley, Wei Bao, Shristi Rawal, Louise G. Grunnet, Anne Cathrine B. Thuesen, James L. Mills, Edwina Yeung, Stefanie N. Hinkle, Wei Zhang, Allan Vaag, Aiyi Liu, Frank B. Hu, Cuilin Zhang

Published in: Diabetologia | Issue 8/2018

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Abstract

Aims/hypothesis

Gestational diabetes mellitus (GDM) is a common complication of pregnancy that has substantial short- and long-term adverse health implications for women and their children. However, large-scale studies on genetic risk loci for GDM remain sparse.

Methods

We conducted a case–control study among 2636 women with GDM and 6086 non-GDM control women from the Nurses’ Health Study II and the Danish National Birth Cohort. A total of 112 susceptibility genetic variants confirmed by genome-wide association studies for type 2 diabetes were selected and measured. A weighted genetic risk score (GRS) was created based on variants that were significantly associated with risk of GDM after correcting for the false discovery rate.

Results

For the first time, we identified eight variants associated with GDM, namely rs7957197 (HNF1A), rs10814916 (GLIS3), rs3802177 (SLC30A8), rs9379084 (RREB1), rs34872471 (TCF7L2), rs7903146 (TCF7L2), rs11787792 (GPSM1) and rs7041847 (GLIS3). In addition, we confirmed three variants, rs10830963 (MTNR1B), rs1387153 (MTNR1B) and rs4506565 (TCF7L2), that had previously been significantly associated with GDM risk. Furthermore, compared with participants in the first (lowest) quartile of weighted GRS based on these 11 SNPs, the ORs for GDM were 1.07 (95% CI 0.93, 1.22), 1.23 (95% CI 1.07, 1.41) and 1.53 (95% CI 1.34, 1.74) for participants in the second, third and fourth (highest) quartiles, respectively. The significant positive associations between the weighted GRS and risk of GDM persisted across most of the strata of major risk factors for GDM, including family history of type 2 diabetes, smoking status, BMI and age.

Conclusions/interpretation

In this large-scale case–control study with women from two independent populations, eight novel GDM SNPs were identified. These findings offer the potential to improve our understanding of the aetiology of GDM, and particularly of biological mechanisms related to beta cell function.
Appendix
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Literature
1.
American Diabetes Association (2004) Gestational diabetes mellitus. Diabetes Care 27(Suppl 1):S88–S90
2.
Zhu Y, Zhang C (2016) Prevalence of gestational diabetes and risk of progression to type 2 diabetes: a global perspective. Curr Diab Rep 16:7CrossRefPubMed
3.
Ng M, Fleming T, Robinson M et al (2014) Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384:766–781CrossRefPubMedPubMedCentral
4.
Bryson CL, Ioannou GN, Rulyak SJ, Critchlow C (2003) Association between gestational diabetes and pregnancy-induced hypertension. Am J Epidemiol 158:1148–1153CrossRefPubMed
5.
Bellamy L, Casas JP, Hingorani AD, Williams D (2009) Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. Lancet 373:1773–1779CrossRefPubMed
6.
Li S, Zhu Y, Yeung E et al (2017) Offspring risk of obesity in childhood, adolescence and adulthood in relation to gestational diabetes mellitus: a sex-specific association. Int J Epidemiol 46:1533–1541CrossRefPubMed
7.
Zhu Y, Olsen SF, Mendola P et al (2016) Growth and obesity through the first 7 y of life in association with levels of maternal glycemia during pregnancy: a prospective cohort study. Am J Clin Nutr 103:794–800CrossRefPubMedPubMedCentral
8.
Hillier TA, Pedula KL, Schmidt MM et al (2007) Childhood obesity and metabolic imprinting: the ongoing effects of maternal hyperglycemia. Diabetes Care 30:2287–2292CrossRefPubMed
9.
Clausen TD, Mathiesen ER, Hansen T et al (2008) High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women with gestational diabetes mellitus or type 1 diabetes: the role of intrauterine hyperglycemia. Diabetes Care 31:340–346CrossRefPubMed
10.
Solomon CG, Willett WC, Carey VJ et al (1997) A prospective study of pregravid determinants of gestational diabetes mellitus. JAMA 278:1078–1083CrossRefPubMed
11.
Tobias DK, Zhang C, Chavarro J et al (2012) Prepregnancy adherence to dietary patterns and lower risk of gestational diabetes mellitus. Am J Clin Nutr 96:289–295CrossRefPubMedPubMedCentral
12.
Zhang C, Solomon CG, Manson JE, Hu FB (2006) A prospective study of pregravid physical activity and sedentary behaviors in relation to the risk for gestational diabetes mellitus. Arch Intern Med 166:543–548CrossRefPubMed
13.
Zhang C, Tobias DK, Chavarro JE et al (2014) Adherence to healthy lifestyle and risk of gestational diabetes mellitus: prospective cohort study. BMJ 349:g5450CrossRefPubMedPubMedCentral
14.
Zhang C, Rawal S, Chong YS (2016) Risk factors for gestational diabetes: is prevention possible? Diabetologia 59:1385–1390CrossRefPubMed
15.
Zhang C, Ning Y (2011) Effect of dietary and lifestyle factors on the risk of gestational diabetes: review of epidemiologic evidence. Am J Clin Nutr 94:1975S–1979SCrossRefPubMedPubMedCentral
16.
Martin AO, Simpson JL, Ober C, Freinkel N (1985) Frequency of diabetes mellitus in mothers of probands with gestational diabetes: possible maternal influence on the predisposition to gestational diabetes. Am J Obstet Gynecol 151:471–475CrossRefPubMed
17.
18.
Tarnowski M, Malinowski D, Pawlak K et al (2017) GCK, GCKR, FADS1, DGKB/TMEM195 and CDKAL1 gene polymorphisms in women with gestational diabetes. Can J Diabetes 41:372–379CrossRefPubMed
19.
20.
Poulsen P, Levin K, Petersen I et al (2005) Heritability of insulin secretion, peripheral and hepatic insulin action, and intracellular glucose partitioning in young and old Danish twins. Diabetes 54:275–283CrossRefPubMed
21.
Zhang C, Bao W, Rong Y et al (2013) Genetic variants and the risk of gestational diabetes mellitus: a systematic review. Hum Reprod Update 19:376–390CrossRefPubMedPubMedCentral
22.
Shaat N, Ekelund M, Lernmark A et al (2005) Association of the E23K polymorphism in the KCNJ11 gene with gestational diabetes mellitus. Diabetologia 48:2544–2551CrossRefPubMed
23.
Shaat N, Lernmark A, Karlsson E et al (2007) A variant in the transcription factor 7-like 2 (TCF7L2) gene is associated with an increased risk of gestational diabetes mellitus. Diabetologia 50:972–979CrossRefPubMed
24.
Wu L, Cui L, Tam WH, Ma RC, Wang CC (2016) Genetic variants associated with gestational diabetes mellitus: a meta-analysis and subgroup analysis. Sci Rep 6:30539CrossRefPubMedPubMedCentral
25.
Zhou Q, Zhang K, Li W et al (2009) Association of KCNQ1 gene polymorphism with gestational diabetes mellitus in a Chinese population. Diabetologia 52:2466–2468CrossRefPubMed
26.
27.
Mahajan A, Go MJ, Zhang W et al (2014) Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46:234–244CrossRefPubMed
28.
Morris AP, Voight BF, Teslovich TM et al (2012) Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 44:981–990CrossRefPubMedPubMedCentral
29.
Voight BF, Scott LJ, Steinthorsdottir V et al (2010) Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 42:579–589CrossRefPubMedPubMedCentral
30.
Manning AK, Hivert MF, Scott RA et al (2012) A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet 44:659–669CrossRefPubMedPubMedCentral
31.
Scott RA, Lagou V, Welch RP et al (2012) Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 44:991–1005CrossRefPubMedPubMedCentral
32.
Dupuis J, Langenberg C, Prokopenko I et al (2010) New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42:105–116CrossRefPubMedPubMedCentral
33.
Zhang C, Hu FB, Olsen SF et al (2014) Rationale, design, and method of the Diabetes & Women's Health study—a study of long-term health implications of glucose intolerance in pregnancy and their determinants. Acta Obstet Gynecol Scand 93:1123–1130CrossRefPubMedPubMedCentral
34.
Lindstrom S, Loomis S, Turman C et al (2017) A comprehensive survey of genetic variation in 20,691 subjects from four large cohorts. PLoS One 12:e0173997CrossRefPubMedPubMedCentral
35.
36.
Solomon CG, Willett WC, Rich-Edwards J et al (1996) Variability in diagnostic evaluation and criteria for gestational diabetes. Diabetes Care 19:12–16CrossRefPubMed
37.
Olsen SF, Houshmand-Oeregaard A, Granstrom C et al (2017) Diagnosing gestational diabetes mellitus in the Danish National Birth Cohort. Acta Obstet Gynecol Scand 96:563–569CrossRefPubMed
38.
World Health Organization (2014) Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: a World Health Organization Guideline. Diabetes Res Clin Pract 103:341–363CrossRef
39.
Hunter DJ, Kraft P, Jacobs KB et al (2007) A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 39:870–874CrossRefPubMedPubMedCentral
40.
Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2010) A basic introduction to fixed-effect and random-effects models for meta-analysis. Res Synth Methods 1:97–111CrossRefPubMed
41.
Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125:279–284CrossRefPubMed
42.
Gan W, Walters RG, Holmes MV et al (2016) Evaluation of type 2 diabetes genetic risk variants in Chinese adults: findings from 93,000 individuals from the China Kadoorie Biobank. Diabetologia 59:1446–1457CrossRefPubMedPubMedCentral
43.
Qi Q, Stilp AM, Sofer T et al (2017) Genetics of type 2 diabetes in U.S. Hispanic/Latino individuals: results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Diabetes 66:1419–1425CrossRefPubMedPubMedCentral
44.
Machiela MJ, Chen CY, Chen C et al (2011) Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. Genet Epidemiol 35:506–514PubMedPubMedCentral
45.
R Development Core Team (2008) R: a language and environment for statistical computing. Vienna, Austria
46.
Sladek R, Rocheleau G, Rung J et al (2007) A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445:881–885CrossRefPubMed
47.
Lauenborg J, Grarup N, Damm P et al (2009) Common type 2 diabetes risk gene variants associate with gestational diabetes. J Clin Endocrinol Metab 94:145–150CrossRefPubMed
48.
Florez JC, Jablonski KA, Bayley N et al (2006) TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med 355:241–250CrossRefPubMedPubMedCentral
49.
Luni C, Marth JD, Doyle FJ 3rd (2012) Computational modeling of glucose transport in pancreatic beta-cells identifies metabolic thresholds and therapeutic targets in diabetes. PLoS One 7:e53130CrossRefPubMedPubMedCentral
50.
Ellard S (2000) Hepatocyte nuclear factor 1 alpha (HNF-1α) mutations in maturity-onset diabetes of the young. Hum Mutat 16:377–385
51.
Nicolson TJ, Bellomo EA, Wijesekara N et al (2009) Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes-associated variants. Diabetes 58:2070–2083CrossRefPubMedPubMedCentral
52.
Chimienti F, Devergnas S, Pattou F et al (2006) In vivo expression and functional characterization of the zinc transporter ZnT8 in glucose-induced insulin secretion. J Cell Sci 119:4199–4206CrossRefPubMed
53.
Yang Y, Chang BH, Samson SL, Li MV, Chan L (2009) The Krüppel-like zinc finger protein Glis3 directly and indirectly activates insulin gene transcription. Nucleic Acids Res 37:2529–2538CrossRefPubMedPubMedCentral
54.
Huopio H, Cederberg H, Vangipurapu J et al (2013) Association of risk variants for type 2 diabetes and hyperglycemia with gestational diabetes. Eur J Endocrinol 169:291–297CrossRefPubMed
55.
Vlassi M, Gazouli M, Paltoglou G et al (2012) The rs10830963 variant of melatonin receptor MTNR1B is associated with increased risk for gestational diabetes mellitus in a Greek population. Hormones (Athens) 11:70–76CrossRef
56.
Liao S, Liu Y, Tan Y et al (2012) Association of genetic variants of melatonin receptor 1B with gestational plasma glucose level and risk of glucose intolerance in pregnant Chinese women. PLoS One 7:e40113CrossRefPubMedPubMedCentral
57.
Mulder H, Nagorny CL, Lyssenko V, Groop L (2009) Melatonin receptors in pancreatic islets: good morning to a novel type 2 diabetes gene. Diabetologia 52:1240–1249CrossRefPubMed
58.
Athanasiadis G, Cheng JY, Vilhjalmsson BJ et al (2016) Nationwide genomic study in Denmark reveals remarkable population homogeneity. Genetics 204:711–722CrossRefPubMedPubMedCentral
Metadata
Title
Genetic variants of gestational diabetes mellitus: a study of 112 SNPs among 8722 women in two independent populations
Authors
Ming Ding
Jorge Chavarro
Sjurdur Olsen
Yuan Lin
Sylvia H. Ley
Wei Bao
Shristi Rawal
Louise G. Grunnet
Anne Cathrine B. Thuesen
James L. Mills
Edwina Yeung
Stefanie N. Hinkle
Wei Zhang
Allan Vaag
Aiyi Liu
Frank B. Hu
Cuilin Zhang
Publication date
01-08-2018
Publisher
Springer Berlin Heidelberg
Published in
Diabetologia / Issue 8/2018
Print ISSN: 0012-186X
Electronic ISSN: 1432-0428
DOI
https://doi.org/10.1007/s00125-018-4637-8

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