Stephanie A. Stein, MD Ravi Kant, MBBS Rana Malek, MD Alan R. Shuldiner, MD
Science is not like a puzzle; the picture we are assembling is changing while we are assembling it.
Key Points
• The genetic underpinnings of gestational diabetes mellitus (GDM) are complex, involving multiple genes that interact with the pregnant milieu and other factors.
• Genes that cause a relatively uncommon form of diabetes, autosomal dominant maturity-onset diabetes of the young (MODY), increase the risk for GDM.
• In addition, common variants that have a modest effect on risk for typical type 2 diabetes mellitus (T2DM) also appear to confer a modest increase in risk for GDM suggesting an overlapping genetic architecture between T2DM and GDM.
• Maternal and fetal genotypes interact to influence fetal birth weight.
• Insights into the genetic architecture of GDM may help to improve prevention and treatment of women with GDM and better maternal and fetal outcomes.
INTRODUCTION
Both type 1 diabetes mellitus (T1DM) and T2DM are multifactorial diseases caused by a combination of genetic and nongenetic risk factors. However, monogenic diabetes syndromes result from inheritance of single-gene mutations. Evidence also supports a genetic component to GDM with observations of concordance among siblings,1 increased prevalence of diabetes mel- litus in family members of those with GDM,2,3 and increased risk for future T2DM in those with GDM.4,5 With advancements in high-throughput DNA-sequencing methodology has come great progress in our knowledge of the human genome. Specifically, sequence variants in a number of genes associated with T2DM and monogenic diabetes have been discovered.6 These breakthroughs have paved the way to begin to understand the genetic contribution to GDM although knowledge is lagging behind. Researchers have begun to investigate how genetic variants associated with T2DM and monogenic diabetes affect predisposition to GDM, and whether distinct variants in these or other genes influence GDM risk.
Understanding the genetic basis of GDM has significant implications for the mother, fetus, and family members. The Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study and other studies demonstrated that maternal hyperglycemia (and even glucose levels within the normal range) is associated with adverse maternal, fetal, and neonatal outcomes.7 By understanding the genetics of GDM, we can better ascertain who is at risk, leading to earlier diagnosis and treatment and hopefully less adverse pregnancy outcomes. In the case of GDM caused by genes known to cause monogenic diabetes syndromes, more targeted therapy may be tailored to the specific gene change. In this chapter, we provide an up-to-date review of the genetic architecture of GDM and how this relates to the genetics of T2DM and monogenic diabetes.
GENES CAUSING MONOGENIC DIABETES SYNDROMES AND THEIR RELATION TO GDM
Monogenic diabetes syndromes can be inherited in an autosomal dominant, autosomal recessive, or mitochondrial (maternal) fashion, or mutations can arise de novo. They are estimated to account for approximately 5% of all diabetes cases.8 Most commonly they result from mutations in genes causing β-cell dysfunction or loss resulting in impaired insulin secretion.9 Less commonly, monogenic diabetes syndromes are due to mutations in genes that cause insulin resistance.10
MODY is the most common monogenic diabetes syndrome, with inheritance in an autosomal dominant manner. Patients typically present with diabetes at a young age (<30 years); furthermore, they are not obese, continue to make insulin, lack T1DM-related autoantibodies, and have other family members with diabetes.11,12 Currently, mutations in at least 13 different genes have been implicated in causing MODY (Table 18-1).13-15 Most commonly MODY is due to a mutation in a transcription factor gene involved in insulin secretion and β-cell developmental pathways.9 The most frequent transcription factor-MODY is MODY3, caused by mutations in TCF1, encoding the transcription factor hepatocyte nuclear factor1a (HNF1a). MODY2, the second most common form of MODY, is caused by mutations in the glucokinase (GCK) gene.11 The enzyme GCK is expressed in pancreatic β-cells and catalyzes the transfer of phosphate from ATP to glucose to form glucose-6-phosphate eventually leading to glucose oxidation, generation of ATP, closure of the ATP-sensitive potassium channels, and release of insulin into the circulation.16 Mutations in GCK result in reduced glucose-stimulated insulin release.11,17
Unfortunately, MODY is often inappropriately diagnosed as T1DM or T2DM due to overlapping features.18 The SEARCH for Diabetes in Youth Study sequenced the most common three MODY genes in antibody-negative subjects diagnosed with diabetes before age 20 and revealed that 94% of individuals with MODY were improperly diagnosed, mostly with T1DM (36%) or T2DM (51%).19 Since it is not uncommon for MODY to present in young adulthood, it would be expected that its diagnosis, when suspected, can frequently be made in pregnant patients screened for GDM. Diagnosis of MODY in patients with GDM can have important implications for treatment and prognosis and for family members. For example, patients with transcription factor-MODY subtypes such as MODY3 and MODY1 experience progressive hyperglycemia usually necessitating treatment to prevent diabetes-related complications. These patients are especially sensitive to the sulfonylurea class of antidiabetic agents.20 Patients with GCK-MODY, on the other hand, experience stable and mildly elevated blood glucoses that most often do not progress or cause complications and therefore do not typically necessitate treatment.
TABLE 18-1 Subtypes of MODY13-15
|
MODY Subtype |
Gene |
Clinical Features |
|
MODY1 |
HNF4A |
Progressive, risk of diabetes-related complications, sensitive to sulfonylureas |
|
MODY2 |
GCK |
Mild, nonprogressive hyperglycemia, low complication risk, no treatment required |
|
MODY3 |
TCF1 (HNF1A) |
Most common, progressive, risk of diabetes-related complications, sensitive to sulfonylureas |
|
MODY4 |
IPF1 (PDX1) |
Very rare |
|
MODY5 |
TCF2 (HNF1B) |
Associated with renal disease (i.e., renal failure, renal cysts) |
|
MODY6 |
NEUROD1 |
Very rare |
|
MODY7 |
KLF11 |
Very rare |
|
MODY8 |
CEL |
Very rare; can cause exocrine pancreatic insufficiency |
|
MODY9 |
PAX4 |
Very rare |
|
MODY10 |
INS |
Can also cause neonatal diabetes |
|
MODY11 |
BLK |
Very rare |
|
MODY12 |
ABCC8 |
Can also cause neonatal diabetes; sensitive to sulfonylureas |
|
MODY13 |
KCNJ11 |
Can also cause neonatal diabetes; sensitive to sulfonylureas |
GCK-MODY and GDM
More research has focused on the association between GDM and GCK-MODY than transcription factor-MODY. Those with GCK-MODY are at a higher risk for development of GDM.21 In addition, it is often during pregnancy that women with GCK-MODY are first recognized as having hyperglycemia and are diagnosed with GDM.22 Approximately 50% of women with GCK-MODY have gestational diabetes and the prevalence of GCK mutations among women presenting with GDM is approximately 5%.23-26 Published prevalence rates have been higher or lower depending on the clinical criteria used to test for GCK mutations. Ellard et al. found GCK mutations in 80% of women with GDM who met four prespecified criteria: persistent fasting hyperglycemia outside pregnancy (5.5-8 mmol/L [99-144 mg/ dL]), an increment less than 4.6 mmol/L (83 mg/dL) during a two-hour oral glucose tolerance test, insulin treatment during at least one pregnancy but subsequently controlled on diet, a history of T2DM, GDM, or fasting hyperglycemia of >5.5 mmol/L (>99 mg/dL) in a first-degree relative.27
Knowing that fetal insulin secretion is a key determinant in fetal growth, Hattersley and Tooke hypothesized that a GCK mutation in the fetus would result in impaired insulin secretion and fetal growth, whereas a GCK mutation in the mother would only result in hyperglycemia, fetal hyperinsulinemia, and increased fetal growth.28 In fact, when birth weight was studied in the presence or absence of GCK mutations in the fetus and the mother, birth weight was 533 g lower (P = .002) in the presence of a fetal mutation and 601 g higher (P = .001) in the presence of a maternal mutation.29 Amongst 21 sibling pairs discordant for the GCK mutation, the child with the mutation in 19 of the pairs had a lower birth weight, with a mean difference of 521 g (P = .0002). Interestingly, no difference in birth weight was seen when both mother and fetus had the GCK mutation compared to when neither were affected, despite significant maternal hyperglycemia and no treatment. Similar findings have been reported in other families.30 These findings suggest that when mother and fetus are concordant for a GCK mutation, less aggressive blood glucose management during pregnancy should be considered. In summary, both maternal and fetal GCK-MODY genotypes can interact and affect birth weight.
Further support for a role of GCK variants in GDM comes from the HAPO study in which a common variant (rs1799884), which has been shown to have a modest effect on T2DM risk,31 is also associated with increased fasting and oral glucose tolerance glucose levels in pregnant women.32 This variant is in the regulatory region of GCK and likely affects expression levels of an otherwise normal glucokinase enzyme.
Transcription Factor-MODY and GDM
Although less studied, the relationship between transcription fac- tor-MODY and GDM has also been examined. Weng et al. found that amongst a population of Swedish women with GDM, the prevalence of transcription factor-MODY was 6%, with women carrying mutations in TCF1 and insulin promoter factor1 (IPF1), resulting in MODY3 and MODY4, respectively.33 Mutations in IPF1 have also been found in members of an Italian family with GDM.34 Another study examined GCK and TCF1 mutations in a Polish population with GDM and found the prevalence to be 2% and 0.8%, respectively.35 Common variants in GCK, TCF1, and HNF4A have been found to be associated with a modest increase in risk for T2DM. Shaat et al. genotyped over 1800 Scandinavian women both with and without GDM for common T2DM susceptibility variants in GCK, TCF1, and HNF4A and found that variants in GCK and TCF1 were associated with increased risk for GDM, whereas the rs2144908, rs2425637, and rs1885088 variants of HNF4A were not.21 These variants in HNF4A have a relatively small effect on T2DM risk (odds ratio [OR] = 1.22-1.27), and thus, sample size may not have been adequate to show a modest or even moderate effect on GDM risk.
Similar to the research done with GCK mutations and birth weight, researchers have examined how transcription factor mutations may play a role. Pearson et al. found that newborns with HNF4A mutations had a median birth weight 790 g greater than nonmutation family members (P < .001), regardless of whether the mutation was inherited from the mother or the father.36 Macrosomia (birth weight > 4000 g) was significantly more common in affected than unaffected newborns (56% vs. 13%, respectively, P < .001) as well as rates of transient neonatal hypoglycemia (15% vs. 0%, respectively, P = .003). The mechanism seems to be fetal hyperinsulinemia, supported by studies of mice with Hnf4a deletions demonstrating hyperinsulinemia in utero and hyperinsulinemic hypoglycemia at birth. Although these findings are unexpected given HNF4A mutations typically lead to a decrease in glucose-induced insulin secretion, the results have been replicated in several other families.37,38 These findings suggest that it might be prudent to offer genetic testing for HNF4A mutations to those with a family history of diabetes, born with macrosomia, and transient neonatal hypoglycemia. Unlike those with HNF4A mutations, TCF1 mutation carriers did not have increased birth weight or neonatal hypoglycemia.36 These findings were confirmed in other families with TCF1 mutations.39
Less common transcription factor mutations have also been studied in relationship to birth weight. Italian newborns with IPF1 mutations born to mothers with a mutation had significantly lower birth weights than an unaffected Italian newborn population (P = .017).34 On the other hand, there was no significant difference in birth weight when unaffected newborns were born to mothers with the mutation compared to an unaffected population (P = .50). Edghill et al. examined patients with neonatal diabetes for mutations in TCF2, which encodes HNF1B, implicated in MODY5, and found that intrauterine growth was significantly decreased in patients born to unaffected mothers with 69% being small for gestational age (P = .006).40
In summary, it appears that both maternal and fetal transcription factor mutations influence birth weight. At this point, findings point to mutation-specific changes but studies are quite limited in number and sample size, and larger population- and mechanism-based studies are needed to better understand this relationship.
SUSCEPTIBILITY ALLELES FOR T2DM AND THEIR RELATION TO GDM
T2DM is a complex disease for which both genes and environment affect predisposition. It is polygenic, meaning there are many susceptibility variants, each with a small effect on risk. The influence of genetic susceptibility can be modified by lifestyle factors so that engaging in healthy eating behaviors and regular physical activity can decrease the risk of diabetes even in people who have a high genetic burden.41 The introduction of genome-wide association studies has allowed for substantial progress in understanding the genetics of T2DM. Currently, there are 70 T2DM susceptibility loci identified,42 most affecting insulin secretion.41,43,44 These variants are common in the population, each conferring a modest increase in risk (OR = 1.1-1.4). Together, they account for only ~10% of the genetic contribution to diabetes leaving most of the inherited risk still to be identified.
A common genetic foundation for T2DM and GDM is suggested by their clustering in families, higher risk for T2DM in those with GDM, and a similar pathophysiology of insulin resistance and impaired insulin secretion.2,3,45,46 In addition to common T2DM susceptibility variants in some of the same genes that cause monogenic diabetes mentioned earlier, common variants in transcription factor 7-like 2 (TCF7L2) and peroxisome prolif- erator-activated receptor-gamma (PPARG) are among the most studied T2DM susceptibility genes that have also been studied in GDM.
Transcription Factor 7-like 2
Association between genetic variants (e.g., rs7903146) in TCF7L2 and T2DM was first demonstrated in an Icelandic population and then replicated in several other cohorts.41,47,48 Compared with noncarriers, heterozygotes and homozygotes with the at-risk alleles were found to have a relative risk of T2DM of 1.45 and 2.41, respectively.47 These findings have been widely replicated in diverse populations. For example, Damcott et al. compared the genotype frequencies of rs7903146 in TCF7L2 in Amish subjects with T2DM, impaired glucose tolerance (IGT), and normal glucose tolerance (NGT).49 When the T2DM/IGT group was compared with the NGT group, there was a strong association with rs7903146 (P = .008; OR = 1.57). The risk (C) allele is common in the population, for example, in Caucasians, approximately 35% and 10% are heterozygotes and homozygotes, respectively; the frequency is even higher in African-derived populations and much lower in Asian populations. TCF7L2 is a transcription factor important for pancreatic islet development and islet function.41,48,49 Consistent with its role in pancreatic islet function, subjects with the risk allele have decreased insulin secretion compared to noncarriers.
Studies have consistently shown this same variant in TCF7L2 to also be associated with risk for GDM. Shaat et al. genotyped the TCF7L2 rs7903146 variant in women with and without GDM and found that heterozygotes and homozygotes had a 1.56- and 2.05-fold increased risk for GDM, respectively, compared with noncarriers.50 In a similar study of a Greek population, the risk associated with this variant in TCF7L2 for GDM was 2.69- and 3.25-fold for heterozygotes and homozygotes, respectively, compared with the wild-type homozygotes.51 Amongst women from the HAPO cohort, those who carried the TCF7L2 rs7903146 variant had a higher risk of GDM (OR = 2.04, 95% confidence interval [CI] 1.38-3.00; P = .003).32
The effect of TCF7L2 polymorphisms on birth weight has also been examined. Among the HAPO cohort,7 there was an association between maternal genotype and higher offspring birth weight but not between fetal genotype and birth weight. Freathy et al. further studied this relationship in other populations and found that a fetal copy of the TCF7L2 rs7903146 susceptibility allele was associated with an 18-g increase in birth weight (P = .001), whereas each maternal copy was associated with a 30-g increase in offspring birth weight (P = 2.8 x 10-5).52 When fetal and maternal genotype effects were adjusted for one another, the effect was predominantly maternal driven with a maternal copy of the allele resulting in impaired insulin secretion, maternal hyperglycemia, fetal hyperin- sulinemia, and therefore increased intrauterine growth. Others have found no association between TCF7L2 fetal genotype and fetal and early postnatal birth weight.53,54 Thus, the burden of evidence suggests a modest effect of maternal TCF7L2 variant on birth weight. Larger and long-term studies will be necessary to further define the importance of TCF7L2 genetics to fetal outcomes.
Peroxisome Proliferator-Activated Receptor-Gamma
A potential role for the nuclear receptor PPARG in T2DM was originally suggested given its involvement in adipocyte differentiation, insulin sensitivity, and as a target for the antidiabetic drug class thiazolidinediones.55,56 Genetic variants in PPARG have also been associated with T2DM risk. The SNP rs1801282 encodes a missense mutation that predicts the substitution of proline to alanine at codon 12 of the PPARy2 isoform (Pro12Ala). Homozygosity for the more common Pro12 allele, present in approximately 75% of Caucasians, is associated with an increased risk for T2DM compared to carriers of the Ala12 allele.57 A meta-analysis of the association found a 1.25-fold increased diabetes risk (P = .002), corresponding to a population attributable risk of 25% for the Pro12 allele.58 On the other hand, the Ala12 allele is associated with a reduction in T2DM risk relative to Pro12 homozygotes (OR = 0.86, 95% CI 0.81-0.90).59 The Ala12 allele is associated with increased insulin sensitivity, presumably the mechanisms whereby it protects from T2DM.
The association between Pro12Ala PPARG and GDM has not been as straightforward. Several studies including that by Pappa et al. of a large Greek population found no significant association between the Pro12Ala polymorphism and GDM.51,60 However, other studies have shown a significant association such as when Chon et al. studied 136 Korean pregnant women and found that those with a Pro12 homozygotes genotype exhibited a 78% higher risk of GDM than Ala12 carriers (P = .027).61 Although the relationship between Pro12 PPARG and GDM is inconclusive, a more consistent association between the Ala12 allele and increased maternal weight during and before pregnancy has been shown.62
Other T2D Susceptibility Variants and GDM
Assuming that the effect size of any single T2D susceptibility variants is similarly small for risk for GDM (OR = 1.1-1.4), large sample sizes would be required to tease out statistically significant differences in allele frequencies between GDM cases and non-GDM controls. Thus, most studies of the role of T2D susceptibility genes in GDM are underpowered. Cho et al. found that Korean patients with GDM were significantly more likely than those without GDM to carry SNPs in the T2DM-associated genetic variants CDKAL1, CDKN2A2B, HHEX, IGF2BP2, SLC30A8, and TCF7L2 with the ORs for GDM not significantly different from those with T2DM.63 Similarly, Lauenborg et al. found ORs greater than 1.0 for common T2DM susceptibility variants in CDKAL1, CDKN2A/2B, HHEX, IGF2BP2, SLC30A8, TCF7L2, FTO, PPARG, TCF2, and KCNJ11 in Danish women with GDM compared to those with NGT.64 Specifically the variants in CDKAL1, TCF7L2, and TCF2 were significantly (P < .05) associated with risk for GDM. Allele summing analysis of the 11 variants revealed increased risk of GDM for carriers of multiple risk alleles on risk of GDM (OR = 1.18 per risk allele, P = 3.2 x 10-6) (Figure 18-1). Women carrying 15 or more risk alleles had a 3.30-fold increased risk of GDM compared with women with 9 or fewer risk alleles (P = 2.8 x 10-4). Further corroborating these findings was a meta-analysis by Mao et al.65 who examined 22 studies that included a total of 10,336 GDM cases and 17,445 controls. Common T2DM susceptibility variants in CDKAL1, TCF7L2, IGF2BP2, MTNR1B, KCNJ11, KCNQ1, and GCK, but not PPARG, were found to be significantly associated with GDM (Table 18-2). Although meta-analyses are prone to publication bias, prevailing evidence suggests that GDM and T2DM share similar genetic backgrounds.66,67
Novel Genes for GDM
To search for genetic variants for GDM distinct from known diabetes variants, Hayes et al. performed the first genome-wide association study of glycemia in 4437 28-week pregnant mothers enrolled in the HAPO study.68 In addition to confirming associations with a number of known T2DM susceptibility gene variants, for example, GCKR, G6PC2, PCSK1, PPP1R3B, MTNR1B, HNF1A, CDKAL1, YPS26A, and ARAP1, two novel loci were identified. Rs4746822 in HKDC1 was associated with two-hour plasma glucose levels (P = 8.26 x 10-13). HKDC1 encodes hex- okinase domain containing 1 for which little is known about its function. Second, rs6517656 in BACE2 was associated with fasting C-peptide levels (P = 3.06 x 10-7). BACE2 encodes beta-site amyloid beta A4 precursor protein cleaving enzyme 2, which may be involved in brain amyloid-beta deposition in disorders such as Alzheimer disease and Down syndrome. BACE2 is expressed in pancreas, but its role in pancreatic amyloid deposition and islet function is not known. Although replication will be required, these data suggest for the first time that the genetic architecture of glucose homeostasis in pregnancy may also have features distinct from T2DM and MODY.
TABLE 18-2 Meta Analysis of T2DM Susceptibility Alleles in GDM
|
No. Data Sets |
No. of Case/ Control |
Risk Allele |
Dominant Model |
|||||||
|
Variants per Gene |
Risk Allele |
Total/ Subgroup |
OR (95% CI) |
P(Z) |
P(Q) |
OR (95% CI) |
P(Z) |
P(Q) |
||
|
PPARG Rs1801282 |
C |
Total |
11 |
2908/6940 |
1.01 (0.96-1.06) |
0.80 |
1.00 |
1.14 (0.68-1.91) |
0.63 |
0.45 |
|
Caucasian |
5 |
1559/5721 |
1.00 (0.94-1.06) |
0.98 |
0.99 |
1.01 (0.58-1.76) |
0.98 |
0.44 |
||
|
East Asian |
4 |
1149/1035 |
1.02 (0.93-1.11) |
0.70 |
0.99 |
2.43 (0.38-15.43) |
0.35 |
0.27 |
||
|
TCF7L2 Rs7903146 |
T |
Total |
6 |
3148/6550 |
1.51 (1.39-1.65) |
<10-5 |
0.77 |
1.69 (1.51-1.89) |
<10-5 |
0.51 |
|
Caucasian |
4 |
1812/4681 |
1.51 (1.38-1.65) |
<10-5 |
0.48 |
1.71 (1.49-1.96) |
<10-5 |
0.28 |
||
|
East Asian |
2 |
1336/1869 |
1.55 (1.16-2.09) |
0.004 |
0.90 |
1.56 (1.24-2.22) |
0.001 |
0.75 |
||
|
MTNR18 Rs10830963 |
G |
Total |
5 |
3094/4111 |
1.34 (1.18-1.52) |
<10-5 |
0.02 |
1.46 (1.25-1.72) |
<10-5 |
0.11 |
|
IGF2BP2 Rs4402960 |
T |
Total |
4 |
2304/5228 |
1.21 (1.08-1.36) |
0.001 |
0.09 |
1.25 (1.07-1.49) |
0.003 |
0.06 |
|
East Asian |
3 |
2030/2894 |
1.24 (1.07-1.44) |
0.004 |
0.07 |
1.27 (1.12-1.43) |
0.0002 |
0.81 |
||
|
KCNJ11 rs5219 |
T |
Total |
5 |
2305/5569 |
1.15 (1.06-1.24) |
0.0004 |
0.99 |
1.25 (1.10-1.42) |
0.001 |
0.88 |
|
Caucasian |
3 |
991/3698 |
1.17 (1.05-1.30) |
0.005 |
0.98 |
1.25 (1.07-1.46) |
0.006 |
0.72 |
||
|
East Asian |
2 |
1314/1871 |
1.13 (1.02-1.26) |
0.03 |
0.93 |
1.11 (1.03-1.20) |
0.02 |
0.29 |
||
|
CDKAL1 rs7754840 |
C |
Total |
4 |
2959/3675 |
1.43 (1.20-1.71) |
<10-4 |
0.0003 |
1.51 (1.33-1.82) |
<10-4 |
0.008 |
|
KCNQ1 rs2237892 |
C |
Total |
3 |
2285/2168 |
1.20 (1.09-1.31) |
<10-4 |
0.70 |
1.42 (1.18-1.71) |
0.0002 |
0.97 |
|
KCNQ1 rs2237895 |
C |
Total |
3 |
2286/2168 |
1.20 (1.09-1.31) |
0.0001 |
0.75 |
1.31 (1.16-1.48) |
<10-4 |
0.54 |
|
GCK rs4607517 |
A |
Total |
5 |
2135/4193 |
1.12 (1.02-1.23) |
0.01 |
0.41 |
1.15 (1.01-1.30) |
0.04 |
0.43 |
Source: Adapted from Mao et al.65
CONCLUSIONS
Tremendous gains in our understanding of the genetic contribution to T2DM and monogenic diabetes have occurred in the last several years. Following this lead have been advances in our knowledge of the genetic foundation of GDM, although we are still in the early stages. Current evidence suggests that relatively uncommon, but large effect MODY-gene mutations increase risk for GDM. Given the high prevalence of GDM in subjects with autosomal dominant GCK-MODY, screening for mutations in GCK in patients with GDM and a family history of T2DM and/or GDM should be considered. In addition, common variants that have a modest effect on T2DM risk also appear to confer a modest increase in risk for GDM suggesting an overlapping genetic architecture between T2DM and GDM. Furthermore, maternal and fetal genotypes seem to interact to influence fetal birth weight. Some of the interactions are well defined, such as GCK mutations and fetal birth weight, but others need further characterization. These insights into the genetic architecture of GDM may help to improve prevention and treatment of women with GDM and better maternal and fetal outcomes. Ultimately larger studies are needed to better define these relationships. In addition, future studies must focus on the discovery of new alleles for GDM and deepening our understanding of underlying mechanisms and pathways they effect.
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