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  • Research article
  • Open Access
  • Open Peer Review

Association between genetic risk variants and glucose intolerance during pregnancy in north Indian women

BMC Medical Genomics201811:64

https://doi.org/10.1186/s12920-018-0380-8

  • Received: 4 March 2018
  • Accepted: 25 July 2018
  • Published:
Open Peer Review reports

Abstract

Background

Gestational diabetes (GDM) is a more common problem in India than in many other parts of the world but it is not known whether this is due to unique environmental factors or a unique genetic background. To address this question we examined whether the same genetic variants associated with GDM and Type 2 Diabetes (T2D) in Caucasians also were associated with GDM in North Indian women.

Methods

Five thousand one hundred pregnant women of gestational age 24–28 weeks from Punjab were studied by a 75 g oral glucose tolerance test (OGTT). GDM was diagnosed by both WHO1999 and 2013 criteria. 79 single nucleotide polymorphisms (SNPs) previously associated with T2D and glycemic traits (12 of them also with GDM) and 6 SNPs from previous T2D associations based on Indian population (some also with European) were genotyped on a Sequenom platform or using Taqman assays in DNA from 4018 women.

Results

In support of previous findings in Caucasian GDM, SNPs at KCJN11 and GRB14 loci were nominally associated with GDM1999 risk in Indian women (both p = 0.02). Notably, T2D risk alleles of the variant rs1552224 near CENTD2, rs11708067 in ADCY5 and rs11605924 in CRY2 genes associated with protection from GDM regardless of criteria applied (p < 0.025). SNPs rs7607980 near COBLL1 (p = 0.0001), rs13389219 near GRB14 (p = 0.026) and rs10423928 in the GIPR gene (p = 0.012) as well as the genetic risk score (GRS) for these previously shown insulin resistance loci here associated with insulin resistance defined by HOMA2-IR and showed a trend towards GDM. GRS comprised of 3 insulin secretion loci here associated with insulin secretion but not GDM.

Conclusions

GDM in women from Punjab in Northern India shows a genetic component, seemingly driven by insulin resistance and secretion and partly shared with GDM in other parts of the world. Most previous T2D loci discovered in European studies did not associate with GDM in North India, indicative of different genetic etiology or alternately, differences in the linkage disequilibrium (LD) structure between populations in which the associated SNPs were identified and Northern Indian women. Interestingly some T2D risk variants were in fact indicative of being protective for GDM in these Indian women.

Keywords

  • Genetics
  • Risk variant
  • Gestational diabetes mellitus
  • Single nucleotide polymorphism
  • Diagnostic criteria
  • Insulin resistance
  • Insulin secretion
  • Type 2 diabetes mellitus

Background

Gestational Diabetes Mellitus (GDM) has been officially defined as “carbohydrate intolerance” of variable severity with onset or first recognition during pregnancy [13] irrespective of treatment and whether or not the condition persists after pregnancy. GDM represents almost 90% of all pregnancies complicated by diabetes [4]. The prevalence of GDM is rapidly increasing, ranging from 2 to 14% depending upon diagnostic criteria [5, 6]. In a study of South Indian women, GDM prevalence varied between 12 and 21% [7] while another study of North Indian women reported a prevalence of 10% using WHO criteria [8]. The hallmark of GDM is increased insulin resistance accompanied by decreased compensatory insulin secretory response. Type 2 diabetes (T2D) is also caused by increased insulin resistance and decreased insulin secretion to compensate for the former. Thus, both T2D and GDM share the same pathophysiology which is influenced by similar risk factors like high body mass index (BMI), history of abnormal glucose intolerance, family history of diabetes, age, and ethnicity [911].

A family history of both T2D and GDM is known to increase GDM risk, indicative of a common genetic component underlying both T2D and GDM [12, 13]. Till date, more than 120 T2D risk loci have been confirmed to be associated with T2D [14]. A large proportion of them have also shown association with GDM. T2D risk variants at the MTNR1B, FTO, TLE1, G6PC2, GCKR, TCF7L2, ADCY5, CDKAL1, TCF2, HNF1B, PPARG, KCNJ11, SLC30A8 loci have previously been associated with GDM in European populations [1518] whereas variants in the CDKAL1, CDKN2A/2B, MTNR1B and KCNQ1 loci were associated with GDM in Korean women [19, 20].

Some genetic variants are more unique to Indian T2D patients e.g. the SGCG (rs9552911) and TMEM163 (rs998451) variants [2125]. However, genetic studies of GDM in India are scarce. The SNPs rs7754840 and rs7756992 in the CDKAL1 gene were associated with GDM in South Indian women [26], while variants in the HMG20A (rs7178572) and HNF4A (rs4812829) genes were associated with both GDM and T2D [27]. The aim of the present study was to investigate whether a panel of known variants previously associated with GDM and T2D in Indian and European populations are associated with GDM in Punjabi women.

Methods

Study population and phenotyping

Five thousand one hundred pregnant women were recruited by applying a multistage random screening in the State of Punjab in North India for GDM. Pregnant women at gestational week 24–28 were randomly selected and recruited [8, 28]. This was part of a WDF supported project titled “Gestational diabetes in Punjab” with the goal to create and implement sustainable awareness, education, screening, intervention and treatment capacities of diabetes in pregnancy (GDM) within the public and private health care system, as well as in the general population in Punjab. The team included a chief research coordinator, an assistant coordinator, doctors, nurses, lab technicians from all selected sites both in private hospitals and public healthcare system. Approval for screening was obtained from DRME, Chandigarh, India. The recruitment sites included Recruitment sites:, Deep Hospital, Model Town, Ludhiana as the epicenter, Shri Rama Charitable Hospital, Ludhiana, Chawla Hospital, Ludhiana, Iqbal Hospital, Ludhiana, Government Medical Colleges and Hospital, Patiala, Amritsar and Faridkot, PHC Verka, Amritsar, Health Centre Bhadsoan, Patiala, Health Centre Faridkot. The project was approved by Independent ethics committee, Ludhiana in 2009. The ethics committee is registered with Office of Drugs Controller General (India) Directorate General of Health Services with Registration no. ECR/525/Inst/PB/2014.

Information was obtained on age, BMI, family history of diabetes, diet, habitat (urban or rural), education and religion. All information material and written consent forms were provided in 3 languages (Hindi, Punjabi & English) and duly signed by the participants. The study protocol was approved by local Ethical Committees. Glucose was measured in venous plasma samples at fasting and at 2 h after a 75 g glucose challenge using glucometers (Accucheck-Roche Diagnostics). Fasting insulin concentrations were determined with ELISA (Diametra, Milan, Italy; intra- and inter-assay variation of < 5.0 and < 10.0%, respectively). The homeostatic model assessment (HOMA2) was used to quantify insulin resistance (HOMA2-IR) and beta-cell function (HOMA2-B) from fasting insulin and glucose values using the HOMA2 calculator v2.2.3 (http://https://www.dtu.ox.ac.uk/homacalculator/) [29]. GDM was diagnosed according to the WHO1999 (FPG ≥7.0 mmol/l and/or 2-h glucose ≥7.8 mmol/l) and the adapted WHO2013 (FPG ≥5.1 and/or 2-h glucose ≥8.5 mmol/l) criteria (ref). The clinical characteristics of subjects are shown in Table 1.
Table 1

Study population characteristics

  

GDM1999

Controls

GDM2013

Controls

N

Mean

±SD

N

Mean

±SD

N

Mean

±SD

N

Mean

±SD

N

Mean

±SD

Age (years)

4018

21.41

3.40

346

21.11

3.59

3672

21.44

3.38

1386

21.68

3.5

2632

21.27

3.34

BMI

4018

24.11

4.34

346

24.28

4.71

3672

24.09

4.30

1386

24.36

4.48

2632

23.97

4.25

Fasting plasma glucose (mmol/l)

4018

4.81

0.76

346

5.53

1.32

3672

4.74

0.65

1386

5.51

0.69

2632

4.44

0.49

Plasma insulin (pmol)

4018

54.25

61.86

346

46.73

42.24

3672

54.96

63.35

1386

52.74

54.44

2632

55.05

65.43

2 h glucose (venous, mmol/l)

4018

6.20

1.37

346

9.15

1.83

3672

5.93

0.92

1386

6.85

1.70

2632

5.86

1.00

homa2_b with steady state glucose and insulin values

3680

104.02

55.71

346

78.01

37.56

3672

106.36

56.49

1386

77.37

38.02

2632

117.92

58.36

homa2_ir with steady state glucose and insulin values

3680

0.97

0.74

346

0.96

0.73

3672

0.97

0.74

1386

1.02

0.79

2632

0.95

0.71

Genotyping

DNA was extracted from frozen and stored buffy coats using (QIAGEN Autopure LS kits. Six SNPs previously associated with GDM or T2D in India [21, 22, 26, 27, 30] (Additional file 2: Table S1) and 79 SNPs previously associated with T2D in Europe and elsewhere from GWAS studies up to 2012 (some of these also with GDM risk from candidate gene studies in GDM populations) were genotyped in the present study (Additional file 2: Table S1) [14] on a Sequenom Mass ARRAY Platform (Sequenom San Diego, CA, USA) PLEX using MALDI-TOF mass spectrometer [31] or Taqman allelic discrimination assays using an ABI Prism 7900 sequence detection system (Applied Biosystems, Foster City, CA, USA). Genotyping was performed at the Lund University Diabetes Centre, Sweden after obtaining permission from ICMR (dated 21 october 2010 and Office of Drugs Controller General (India)(dated 14/12/2010).

Replication genotyping of 6% of the samples showed > 98% concordance. rs6467136, and rs7202877 had a Hardy-Weinberg equilibrium (HWE) p-value of < 0.001 in unaffected women based on WHO1999 criteria and < 0.05 in unaffected women based on WHO2013 criteria and were hence removed from the analysis.

Statistical analyses

Association of selected SNPs with risk of GDM was assessed by logistic regression analysis adjusted for maternal age and BMI and results presented as ORs with 95% confidence intervals (CI). We also tested for associations with fasting and 2-h glucose values as well as with fasting insulin and HOMA2-B and HOMA2-IR (Additional file 2: Table S1) using linear regression analysis with maternal age and BMI as covariates. Individuals with missing data were excluded. Data were logarithmically transformed before analysis. The power to detect association with GDM2013 including 1386 GDM women and 2632 controls at p < 0.0006 (0.05/79) (after Bonferroni correction) for a SNP allele frequency of 0.3 and effect size 1.3 was 0.97, which decreased to 0.64 for effect size 1.2 under an additive model. For GDM1999, with 346 GDM and 3672 controls, the corresponding figures were 0.39 and 0.12 respectively. For association with quantitative glucose traits, power to detect association was 1 at alpha 0.05 for and allele frequency of 0.3 [32, 33]. A p-value of ≤0.05 was considered statistically significant on account of the current analyses being replication of previously published associations.

Genetic risk scores for insulin secretion (HOMA-2B) and insulin resistance (HOMA-2IR) were calculated using SNPs previously associated with insulin secretion and insulin resistance. SNPs were assessed for linkage disequilibrium (LD) and for those in high LD (r2), only one representative SNP was retained. Individual scores were calculated based on number of risk alleles weighed by their effect sizes reported in previous GWAS studies and logistic regression was performed against normalized measures of insulin secretion and insulin resistance.

All calculations were implemented in STATA, plink 1.09 and SPSS v22.0.

Results

Among the 4018 genotyped women, applying the WHO2013 criteria resulted in a total of 1386 women with GDM (34.5%) whereas the number was reduced to 346 (8.6%) when WHO1999 criteria were used. Notably, only 283 (7.0%) women were diagnosed using both GDM 2013 and GDM 1999 criteria (Additional file 1: Figure S1) [34]. This is concordant with our previously published reports on the larger subset of the same population comprising 5100 women [28]. HOMA2-B was lower in GDM women defined by both criteria compared to pregnant normal glucose tolerant women (PNGT). HOMA2-IR was also higher in women with GDM2013 who thereby were more insulin resistant than PNGT (Table 1).

SNPs previously associated with GDM/T2D in India

None of the 8 SNPs previously associated with GDM or T2D in Indian populations was here associated with GDM (Table 2). However, analysis for association with GDM1999 or GDM 2013 against controls who did not satisfy either criterion revealed the nominal association of rs7756992 in CDKAL1 while rs689 in INS showed a trend towards association with GDM2013 (Table 3).
Table 2

Association of previously reported GDM and T2D loci from Indian population based studies with risk of GDM according to both criteria

Genotype

EA

Chr

Gene/nearest gene

Location

OR_WHO1999

lower CI

upper CI

p_who1999

OR_WHO2013

lower CI

upper CI

p_who2013

n

rs998451

A

2

TMEM163

intron

0.987

0.795

1.224

0.902

0.959

0.843

1.09

0.518

3882

rs1799999

A

7

PPP1R3A

missense

0.862

0.728

1.02

0.083

0.997

0.905

1.098

0.953

3890

rs689

A

11

INS

5’UTR

1.077

0.879

1.319

0.474

1.033

0.914

1.167

0.603

3903

rs9552911

A

13

SGCG

intron

1.057

0.83

1.347

0.653

1.017

0.875

1.183

0.824

3890

rs4812829

A

20

HNF4A

intron

1.04

0.871

1.24

0.667

0.988

0.89

1.096

0.814

3801

rs7178572

G

15

HMG20A

intron

0.988

0.832

1.173

0.891

1.017

0.921

1.122

0.743

3541

rs7756992

G

6

CDKAL 1

intron

0.91

0.75

1.1

0.34

0.97

0.87

1.08

0.64

3686

rs7754840

C

6

CDKAL1

intron

0.87

0.72

1.06

0.17

0.96

0.86

1.07

0.51

3721

EA effect allele, OR_WHO1999 odds ratio based on WHO1999 criteria, OR_WHO2013 Odds ratio based on WHO2013 criteria, CI confidence interval

Table 3

Association of previously reported GDM loci with risk of GDM according to both criteria

SNP

EA

Chr

Gene/nearest gene

Location

WHO 1999

WHO 2013

n

OR

CI(lower)

CI(upper)

p-value

OR

CI(lower)

CI(upper)

p-value

rs9939609

A

16

FTO

intron

1.04

0.86

1.26

0.67

0.98

0.88

1.10

0.83

3120

rs2796441

G

9

TLE 1

intergenic

0.99

0.84

1.16

0.92

1.07

0.97

1.17

0.15

3905

rs560887

C

2

G6PC2/ABCB11

intron

1.18

0.92

1.52

0.19

1.11

0.96

1.28

0.13

3910

rs11708067

A

3

ADCY5

intron

0.98

0.81

1.18

0.86

0.88

0.79

0.99

0.037

3877

rs1111875

C

10

HHEX

intergenic

0.90

0.77

1.06

0.22

1.05

0.96

1.16

0.24

3901

rs10811661

T

9

CDKN2A/2B

intergenic

0.99

0.77

1.26

0.93

1.08

0.94

1.25

0.23

3890

rs4402960

T

3

IGF2BP2

intron

1.02

0.87

1.20

0.77

0.95

0.86

1.04

0.29

3750

rs13266634

C

8

SLC30A8

coding-missense

0.96

0.79

1.17

0.75

0.97

0.87

1.08

0.61

3898

rs7903146

T

10

TCF7L2

Intronic/promoter

1.13

0.95

1.35

0.14

1.01

0.916

1.12

0.76

3543

rs10830963

G

11

MTNR1B

intron

0.89

0.75

1.05

0.20

0.98

0.89

1.08

0.69

3714

rs1801282

C

3

PPARG

Coding-missense

0.86

0.89

1.12

0.22

0.99

0.93

1.08

0.21

3652

rs10010131

G

4

WFS1

intron

1.13

0.95

1.36

0.16

0.99

0.90

1.10

0.99

3843

rs5219

T

11

KCNJ11

coding-missense

1.21

1.03

1.42

0.019

1.00

0.90

1.10

0.99

3595

EA effect allele, OR_WHO1999 odds ratio based on WHO1999 criteria, OR_WHO2013 Odds ratio based on WHO2013 criteria, CI confidence interval

significant p values where p < 0.05 are indicated in bold

Previously reported GDM risk loci

Out of 12 selected previously reported GDM risk loci, the T allele of the missense SNP rs5219 in the KCNJ11 gene was nominally associated with GDM1999 (p = 0.019) (Table 4). Contrary to previous reports, the risk allele A of SNP rs11708067 in the ADCY5 gene showed reduced risk for GDM defined by 2013 (p = 0.037) (Table 4) but not by 1999 criteria. The SNP rs2796441 in the TLE1 gene was associated with decreased insulin secretion (p = 0.013) (Additional file 2: Table S2). The rs13266634 at SLC30A8 locus associated with GDM1999 while SNPs rs5219 in KCNJ11 and rs11708067 in ADCY5 associated with GDM2013 nominally when controls satisfying neither GDM diagnosis criteria were considered (Table 3).
Table 4

Association of previously reported T2D loci with risk of GDM according to both criteria

SNP

EA

Chr

Gene/nearest gene

Location

WHO 1999

WHO 2013

n

OR

CI(lower)

CI(upper)

p-value

OR

CI(lower)

CI(upper)

p-value

rs2296172

G

1

MACF1

coding-missense

0.92

0.71

1.20

0.56

1.04

0.89

1.21

0.58

3847

rs340874

C

1

PROX1

intergenic

0.94

0.80

1.11

0.52

0.96

0.87

1.06

0.47

3709

rs7578597

T

2

THADA

coding-missense

0.90

0.72

1.12

0.37

0.92

0.80

1.06

0.27

3710

rs243088

T

2

BCL 11A

intergenic

1.10

0.94

1.29

0.22

1.07

0.97

1.18

0.15

3717

rs7593730

T

2

RBMS1/ITGB6

intronic

1.01

0.84

1.22

0.83

0.99

0.88

1.11

0.93

3906

rs7607980

C

2

COBLL1

coding-missense

0.95

0.73

1.24

0.75

0.95

0.81

1.11

0.52

3885

rs13389219

C

2

GRB14

intergenic

1.25

1.03

1.52

0.022

1.11

0.99

1.23

0.058

3829

rs7578326

A

2

KIAA1486/IRS1

intron of uncharacterized LOC646736

0.97

0.80

1.18

0.78

0.98

0.87

1.10

0.79

3600

rs2943641

C

2

IRS1

intergenic

0.92

0.76

1.12

0.43

0.97

0.87

1.09

0.67

3643

rs4675095

A

2

IRS1

intron

1.11

0.87

1.42

0.39

1.04

0.90

1.19

0.58

3817

rs831571

C

3

PSMD6

intergenic

1.02

0.84

1.25

0.77

0.93

0.83

1.05

0.26

3726

rs4607103

C

3

ADAMTS9-AS2

intron

1.14

0.98

1.33

0.08

1.00

0.91

1.09

0.97

3884

rs11920090

T

3

SLC2A2

intron

1.19

0.93

1.51

0.16

1.16

1.01

1.33

0.03

3606

rs6815464

C

4

MAEA

intron

1.04

0.83

1.30

0.71

1.03

0.90

1.18

0.64

3722

rs459193

G

5

ANKRD55

intergenic

0.99

0.84

1.16

0.90

1.07

0.97

1.18

0.16

3884

rs4457053

G

5

ZBED3

intron of ZBED3-AS1

1.05

0.86

1.29

0.57

0.95

0.84

1.07

0.45

3579

rs9470794

C

6

ZFAND3

intron

1.07

0.85

1.35

0.51

1.05

0.91

1.21

0.48

3608

rs17168486

T

7

DGKB

intergenic

0.99

0.83

1.17

0.92

0.97

0.88

1.07

0.62

3855

rs2191349

T

7

DGKB/TMEM195

intergenic

1.04

0.88

1.22

0.62

1.00

0.91

1.10

0.95

3903

rs864745

T

7

JAZF1

intron

0.98

0.83

1.16

0.87

1.02

0.92

1.13

0.68

3876

rs4607517

A

7

GCK

intergenic

1.04

0.82

1.32

0.70

1.01

0.88

1.16

0.86

3903

rs17133918

C

7

GRB10

intron

1.03

0.87

1.23

0.67

0.97

0.88

1.08

0.65

3907

rs933360

A

7

GRB10

intron

1.03

0.87

1.22

0.70

1.03

0.93

1.14

0.54

3905

rs6943153

C

7

GRB10

intron

0.86

0.73

1.03

0.11

0.95

0.86

1.05

0.36

3602

rs516946

C

8

ANK1

intron

1.01

0.82

1.23

0.91

1.09

0.97

1.23

0.13

3922

rs896854

T

8

TP53INP1

intron

0.97

0.83

1.14

0.75

0.97

0.88

1.06

0.57

3903

rs7034200

A

9

GLIS3

intron

0.98

0.83

1.15

0.84

1.03

0.93

1.13

0.52

3868

rs13292136

C

9

TLE4 (CHCHD9)

intergenic

0.94

0.75

1.18

0.62

0.98

0.86

1.12

0.79

3706

rs12571751

A

10

ZMIZ1

intron

0.86

0.73

1.01

0.07

0.96

0.87

1.06

0.49

3601

rs553668

A

10

ADRA2A

UTR-3

1.17

0.99

1.39

0.06

1.07

0.97

1.19

0.15

3666

rs10885122

G

10

ADRA2A

intergenic

1.03

0.84

1.27

0.75

1.05

0.93

1.18

0.42

3683

rs163184

G

11

KCNQ1

intron

0.90

0.76

1.07

0.23

1.00

0.90

1.10

0.98

3713

rs2237895

C

11

KCNQ1

intron

0.96

0.81

1.13

0.66

1.01

0.92

1.11

0.79

3682

rs11605924

A

11

CRY2

intron

0.84

0.72

0.97

0.025

1.00

0.92

1.10

0.85

3909

rs7944584

A

11

MADD

intron

0.91

0.74

1.13

0.41

1.09

0.96

1.23

0.15

3553

rs174550

T

11

FADS1

intron

0.94

0.76

1.17

0.62

0.96

0.85

1.09

0.56

3908

rs1552224

A

11

CENTD2

intergenic

0.92

0.75

1.13

0.45

0.81

0.72

0.92

0.001

3911

rs11063069

G

12

CCND2

intergenic

0.99

0.80

1.23

0.98

1.04

0.91

1.19

0.52

3671

rs10842994

C

12

KLHDC5

intergenic

1.13

0.89

1.44

0.28

0.97

0.84

1.11

0.67

3906

rs1153188

A

12

DCD

intergenic

1.15

0.93

1.42

0.19

1.01

0.89

1.14

0.82

3912

rs1531343

C

12

HMGA2

intron of pseudogene

0.83

0.67

1.03

0.09

0.90

0.80

1.02

0.10

3915

rs7961581

C

12

TSPAN8,LGR5

intergenic

0.91

0.77

1.08

0.31

1.02

0.92

1.13

0.61

3703

rs7957197

T

12

OASL/TCF1/HNF1A

intron of QASL

0.87

0.65

1.17

0.37

1.00

0.83

1.21

0.96

3924

rs17271305

G

15

VPS13C

intron

1.02

0.86

1.20

0.81

0.92

0.83

1.02

0.15

3825

rs11071657

A

15

FAM148B

intergenic

1.03

0.87

1.22

0.72

0.92

0.83

1.02

0.13

3897

rs7177055

A

15

HMG20A

intergenic

1.00

0.85

1.17

0.99

0.98

0.89

1.08

0.74

3907

rs35767

G

12

IGF1

nearGene-5

0.88

0.91

1.10

0.19

0.93

0.94

1.06

0.21

3910

rs11634397

G

15

ZFAND6

intergenic

0.89

0.76

1.04

0.16

0.96

0.87

1.06

0.47

3910

rs8042680

A

15

PRC1

intron

0.89

0.76

1.04

0.16

0.99

0.90

1.10

0.95

3887

rs8090011

G

18

LAMA1

intron

0.95

0.81

1.11

0.57

0.93

0.84

1.02

0.13

3911

rs10401969

C

19

SUGP1

intron

0.96

0.72

1.27

0.79

0.86

0.72

1.01

0.07

3605

rs8108269

G

19

GIPR

intergenic

1.02

0.85

1.23

0.77

1.07

0.96

1.19

0.16

3508

rs10423928

A

19

GIPR

intron

0.85

0.67

1.08

0.20

1.06

0.93

1.20

0.37

3911

rs6017317

G

20

FITM2-R3HDML-HNF4A

intergenic

0.96

0.81

1.13

0.64

0.98

0.89

1.08

0.72

3758

rs5945326

A

X

DUSP9

intergenic

0.95

0.81

1.12

0.58

1.01

0.92

1.12

0.74

3589

EA effect allele, OR_WHO1999 odds ratio based on WHO1999 criteria, OR_WHO2013 Odds ratio based on WHO2013 criteria, CI confidence interval

significant p values where p < 0.05 are indicated in bold

Previously reported T2D loci

The risk allele C of SNP rs13389219 in the GRB14 gene was associated with GDM1999 (p = 0.022) (Table 5) but not with GDM2013 (p = 0.058) (Table 5). The T2D risk allele T of SNP rs11920090 in the intron of the SLC2A2 gene was associated with GDM2013 (p = 0.030) (Table 5).
Table 5

Sensitivity analysis for association of selected risk variants with GDM risk

SNP

EA

Chr

Gene/nearest gene

Location

WHO 1999

WHO 2013

n

OR

CI(lower)

CI(upper)

p-value

n

OR

CI(lower)

CI(upper)

p-value

rs13266634a

T

8

SLC30A8

coding-missense

1.24

1.01

1.53

0.037

2834

1.049

0.91

1.21

0.50

3837

rs11605924

A

11

CRY2

intron

0.84

0.71

0.99

0.038

2833

1.005

0.91

1.10

0.91

3848

rs35767

T

12

IGF1

nearGene-5

1.26

1.00

1.60

0.054

2837

1.15

0.98

1.33

0.07

3848

rs5219a

T

11

KCNJ11

coding -missense

1.18

1.00

1.40

0.059

2605

1.00

0.91

1.11

0.91

3539

rs11708067a

G

3

ADCY5

intron

1.11

0.86

1.44

0.42

2810

1.25

1.09

1.45

0.002

3816

rs689a

A

11

INS

Promoter/intron

0.91

0.64

1.29

0.60

2835

0.81

0.65

1.00

0.054

3842

rs8108269

G

19

GIPR

intergenic

1.14

0.94

1.36

0.17

2568

1.12

0.99

1.25

0.059

3449

rs7756992a

G

6

CDKAL1

intron

0.96

0.76

1.19

0.69

2670

2.80

1.00

7.87

0.049

3626

aindicates loci previously associated with GDM / T2D in India or GDM in studies based on the European population

Logistic regression was performed on GDM cases diagnosed according to WHO1999 and WHO2013 criteria against controls who had no GDM diagnosis using either criteria

significant p values where p < 0.05 are indicated in bold

Surprisingly, the T2D risk allele A of SNP rs11605924 in the CRY2 gene was associated with reduced risk of GDM1999 (p = 0.025) (Table 5). The same variant associated with GDM1999 in a sensitivity analysis when controls meeting neither GDM diagnosis criteria were considered (Table 3). In support of this, the same allele was also associated with lower 2-h glucose levels (p = 0.038) (Additional file 2: Table S3).

The risk allele A of SNP rs1552224 in the CENTD2 locus was associated with decreased risk of GDM2013 (p = 0.001) (Table 5).

Association with insulin secretion and insulin resistance

Twelve SNPs previously associated with insulin secretion were here tested for association with HOMA2-B. The T2D risk allele A of rs11071657 at the FAM148B locus was nominally associated with increased insulin secretion (p = 0.044) (Table 6). A GRS comprising of 3 previously reported insulin secretion loci with the lowest p-values for insulin secretion in the present study associated with insulin secretion in the present study (p = 0.008, beta = 0.25, SE = 0.098). GRS for insulin secretion did not associate with either GDM2013 (p = 0.15, beta = − 0.06, SE = 0.045) or GDM1999 (p = 0.73, beta = − 0.009, SE = 0.026).
Table 6

Association of selected loci with insulin secretion (HOMA2-B)

SNP

EA

Chr

Gene/nearest gene

Location

Beta

SE

p-value

N

rs340874

C

1

PROX1

intergenic

0.009

0.011

0.388

3395

rs560887

C

2

G6PC2/ABCB11

intron

−0.004

0.016

0.818

3578

rs11708067

A

3

ADCY5

intron

−0.024

0.012

0.053

3556

rs11920090

T

3

SLC2A2

intron

−0.014

0.015

0.361

3301

rs4607517

A

7

GCK

intergenic

0.007

0.012

0.571

3372

rs2191349

T

7

DGKB/TMEM195

intergenic

−0.008

0.011

0.480

3575

rs7034200

A

9

GLIS3

intron

0.002

0.016

0.922

3576

rs10885122

G

10

ADRA2A

intergenic

−0.006

0.010

0.546

3545

rs7944584

A

11

MADD

intron

−0.021

0.013

0.116

3372

rs7903146

T

10

TCF7L2

Intronic/promoter

0.003

0.011

0.798

3240

rs10830963

G

11

MTNR1B

intron

−0.007

0.011

0.473

3398

rs174550

T

11

FADS1

intron

0.011

0.014

0.435

3248

rs7756992

G

6

CDKAL1

intron

0.011

0.014

0.446

3576

rs11071657

A

15

FAM148B

intergenic

−0.023

0.011

0.044

3568

significant p values where p < 0.05 are indicated in bold

Of 6 SNPs previously associated with measures of insulin resistance, 3 SNPs here associated with HOMA2-IR. The C allele of rs7607980 in the COBLL1 gene was associated with decreased HOMA2-IR (p = 0.0001). The C allele of rs13389219 near GRB14 (p = 0.026) and A allele of rs10423928 in the intron of the GIPR gene (p = 0.012) showed worse insulin resistance (increased HOMA2-IR; Table 7). Genetic risk scores (GRS) calculated based on the 3 SNPs associated with insulin resistance showed an increase of insulin resistance by 0.07 (SE = 0.145, p = 0.006) per allele. GRS for insulin resistance showed a trend towards GDM2013 (p = 0.065, beta = 0.076, SE = 0.04) but not GDM1999 (p = 0.14, beta = 0.023, SE = 0.025).
Table 7

Association with HOMA-IR selected loci: insulin resistance SNPs

SNP

EA

Chr

Gene/nearest gene

Location

Beta

SE

p-value

N

rs2943641

C

2

IRS1

intergenic

−0.001

0.014

0.923

3337

rs4675095

A

2

IRS1

intron

−0.028

0.017

0.102

3500

rs4607517

A

7

GCK

intergenic

0.018

0.018

0.299

3576

rs7607980

C

2

COBLL1

coding-missense

0.070

0.019

0.0001

3557

rs13389219

C

2

GRB14

intergenic

0.029

0.013

0.026

3518

rs10423928

A

19

GIPR

intron

0.041

0.016

0.012

3585

significant p values where p < 0.05 are indicated in bold

Discussion

In this large study, we investigated the genetic basis of gestational diabetes mellitus in Punjabi Indian women [15, 16, 19, 27].

Surprisingly, the genetic variants in the HMG20A and HNF4A genes which previously have been associated with risk of T2D and GDM in South India [27] were not associated with GDM or T2D in Punjabi pregnant women. This could be due to differences in allele frequencies between the North and South Indian populations, which are ethnically quite distinctive populations [35]. The Punjabi Indian population belongs to the “Ancestral North Indians” group and shares genetic similarities with populations from Middle East, Central Asia and to some degree Europe whereas the South Indian population genetically belongs to the distinct “Ancestral South Indian” group [35]. Notably the CDKAL1 variant associated with GDM only when a sensitivity analysis was performed using controls that had no GDM diagnosis using either GDM1999 or GDM2013 criteria, thus replicating a previous association.

Neither did we observe associations with loci associated with GDM elsewhere including variants in the CDKAL1 and MTNR1B loci, which have been reported to be associated with GDM in South Korea [19]. A sensitivity analysis using controls that had no GDM diagnosis using either criterion revealed the nominal association of variants in SLC30A8, KCNJ11 and ADCY5. These largely negative findings could be attributed to population-based differences. Previous studies have indicated differences in anthropometry between Indian and European populations, with the former manifesting a “thin-fat” phenotype [36]. Subsequently, it is possible that since most T2D loci were identified in European ancestry cohorts, the negative findings could reflect differences in tagging SNPs due to differences haplotypes between populations. On the other hand, the underlying etiology of GDM could also be different genetically. While the study population is the largest GDM study till date, this might lack sufficient power to detect genome-wide significance levels of association with an unstable phenotype. The effect sizes of previously reported T2D loci were low, generally under odds ratios of 1.2, therefore the study was not sufficiently powered to demonstrate association of SNPs with such low effect sizes. Alternately, considering the lack of consensus for GDM diagnosis criteria worldwide, it is plausible that this could be due to different thresholds that might apply for the Indian population.

Notably, T2D risk variants in the CRY2 (WHO1999), CENTD2 (WHO2013) and the ADCY5 (WHO2013) genes were here protective for GDM. CRY2 encodes for the cryptochrome protein involved in the regulation of the circadian clock. Risk allele carriers of the rs11708067 SNP in ADCY5 has previously been shown to reduce ADCY5 expression in pancreatic beta cells and important for coupling glucose to insulin secretion in human islets [37]. It has been previously shown that T2D risk alleles show extreme directional differentiation across various populations, with T2D risk alleles decreasing in frequency along human migration into East Asia [38]. Such flip-flops of risk alleles may be explained by population differences, possibly due to genetics or environment. Alternately, such “flip-flop” associations have also been attributed to multi-locus effects as shown from theoretical modeling studies demonstrating that the direction of allelic effect may flip when tested allele is inversely correlated with another risk allele at another locus, or positively correlated with a protective allele at another locus [39].

A HWE threshold of < 0.001 in unaffected individuals based in either criteria was set as a cut-off; SNPs showing significant deviations from HWE should be interpreted with caution, since these could be indicative of population substructures, inbreeding or selection. The current study only comprises genotyping data from candidate SNPs which do not provide sufficient coverage of the genome to detail population stratification or inbreeding. HWE could also be indicative of actual association. A serious problem in the study of the genetics of GDM is the implementation of different criteria, since some women could be classified as controls based on different criteria. For SNP rs5219 in KCNJ11 (HWE p = 0.004, WHO1999; HWE p = 0.01, WHO2013) and rs11605924 in CRY2 (HWE p = 0.007 WHO1999 and HWE p = 0.06, WHO2013), HWE values were nominally significant for the same criteria where an association was observed; these findings need to be replicated in independent cohorts.

Of 6 loci previously associated with insulin resistance, here 3 also showed an association with HOMA2-IR and a trend towards significance for GDM2013 but not GDM1999 including SNPs rs7607980 in the COBLL1 gene [40], rs13389219 near GRB14 and rs10423928 in the GIPR gene indicating that some of the genetic basis seem to be driven by previously reported insulin resistance loci. Similarly, a GRS with the 3 variants with the lowest p-values for insulin secretion associated with insulin secretion but not GDM2013 or GDM1999.

Taken together, the results demonstrate that GDM in women from Punjab in Northern India shows a genetic component, partially shared with GDM in other parts of the world, and seems to be driven by both insulin resistance and secretion. However, the direction of the effect can differ; some T2D risk variants were indicative of being protective for GDM in these Indian women.

Conclusions

GDM in women from Punjab in Northern India shows a genetic component shared with T2D. This genetic basis is seemingly driven by a complex interplay between insulin secretion and sensitivity during pregnancy and is at least partly shared with GDM in other parts of the world. Interestingly some of the T2D risk variants in ADCY5 and CRY2 were protective against GDM. Most of the previous T2D loci discovered in European studies did not associate with GDM in North India. Interestingly some T2D risk variants were in fact indicative of being protective for GDM in these Indian women. This could be attributed to different genetic etiology or differences in the LD structure between populations in which the associated SNPs were identified and Northern Indian women. GWAS or whole genome sequencing will be interesting to further unravel the genetic basis of GDM in India.

Abbreviations

GDM: 

Gestational diabetes mellitus

GRS: 

Genetic risk score

GWAS: 

Genome wide association study

HOMA2: 

Homeostatic model assessment

HOMA2-B: 

Homeostatic model assessment for insulin secretion

HOMA2-IR: 

Homeostatic model assessment for insulin resistance

LD: 

Linkage disequilibrium

OGTT: 

Oral glucose tolerance test

SNP: 

Single nucleotide polymorphism

T2D: 

Type 2 diabetes

Declarations

Acknowledgements

We wish to thank the World Diabetes Foundation for providing a database in Punjab, India and Mr. Raman Gautam for coordinating screening and sampling, Dr. Baldeep and his team from Deep Hospital, Ludhiana, India for providing the infrastructure for the study and the government health authorities of Punjab for supporting the study. We gratefully acknowledge Gabriella Gremsperger, Maria Sterner, Malin Neptin and Jasmina Kravic for their technical assistance, sampling and organization of data. Finally, we thank all the participating pregnant women in the study.

Funding

Funding was received from the World Diabetes Foundation, Denmark, the Danish Strategic Research Council, Novo Nordisk Foundation, the Augustinus Foundation, Center for Physical Activity Research and by Deep Hospital and Ved Nursing Home and Eye Hospital, Ludhiana, India, Sydvästra Skånes Diabetesförening, the Swedish Research Council, Hospital Region of Region Skåne, the Swedish Research Council Networking Grant and the European Research Council.

Availability of data and materials

All data generated or analysed during this study are included in this published article. Individual level genotyping datasets generated and/or analysed during the current study are not publicly available due being part of other ongoing work but are available from the corresponding author on reasonable request.

Authors’ contributions

GPA, PA, CB and RPB researched data, and reviewed/edited the manuscript. GPA, RT, RPB, and AAV acquired data. RPB, GPA, AAV, RT, LG contributed to study design and reviewed/edited the manuscript. LG and AAV contributed to the discussion and extensively reviewed/edited the manuscript. All authors have read and approved the manuscript. RPB wrote the manuscript. RPB and LG take responsibility for the contents of the article.

Ethics approval and consent to participate

The study protocol was approved by local Ethical Committees in Punjab and Lund University, complied with the Declaration of Helsinki (2003). All participates gave written informed consent to take part in the present study.

Consent for publication

This manuscript does not contain individual person’s data in any form. Data presented is not identifiable.

Competing interests

AAV is employed at the Translational Research and Early Clinical Development, Cardiovascular and Metabolic Research, AstraZeneca, Mölndal, Sweden, On behalf of all the authors, Dr. Prasad B has nothing to disclose.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Deep Hospital, Ludhiana, Punjab, India
(2)
Department of Clinical Sciences, Clinical Research Centre, Lund University, Malmö, Sweden
(3)
Department of Endocrinology (Diabetes and Metabolism), Rigshospitalet, Copenhagen, Denmark
(4)
Finnish Institute of Molecular Medicine (FIMM), Helsinki University, Helsinki, Finland
(5)
Cardiovascular and Metabolic Disease (CVMD) Translational Medicine Unit, Early Clinical Development, IMED Biotech Unit, AstraZeneca, Gothenburg, Sweden

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Copyright

© The Author(s). 2018

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