Genes Associated with Metabolism in Gestational Diabetes Mellitus:

A New Categorization According to Risk Factors

Qianyan Zheng

School of Public Health, University of Washington, Seattle, WA, 98105, U.S.A.

Keywords: Genes, Metabolism, GDM, Risk Factors.

Abstract:

Gestational Diabetes Mellitus (GDM) is a rising public health concern with a highly increased prevalence

over the last decade. Previous genetic studies on GDM mainly focused on identifying genes associated with

the shared genetic architecture between Type 2 Diabetes Mellitus (T2DM) and GDM. There is a relative lack

of research on the unique genetic architecture of GDM. Thus, to shed light on the traits of GDM, this review

provided a new categorization of genes with determined association with GDM based on their correspondence

to some important risk factors, through combining out the related references. It was concluded that most

genetic evidence concentrated in a history of GDM and a strong family history of diabetes. Evidence in

obesity, polycystic ovary syndrome (PCOS), and ethnicity gave insights on other underlying mechanisms of

GDM that are worth exploration.

1 INTRODUCTION

Diabetes Mellitus is one of most significant public

health concerns because of its wide age range and

diverse complications. As one of the three essential

classifications of Diabetes Mellitus, although GDM

experiences a similar increase in the prevalence as

T2DM, this medical complication did not receive as

much exclusive research.

To date, most genetic studies on GDM were

extended research based on genes previously

demonstrated to be associated with T2DM.

Therefore, it cannot be denied that there may be a

focus bias leading to the much higher number of

genes related to the shared metabolic mechanisms

with T2DM compared to genes uniquely related to

GDM. In short, research on genetics about exclusive

traits of GDM is insufficient. This review introduced

genes associated with GDM and categorized these

genes according to their corresponding risk factors—

history of GDM and a strong family history of

diabetes, obesity, PCOS, and ethnicity. New insights

on the underlying genetic mechanisms of GDM and

further identification of more genes unique for GDM

are in hope. Such identification could contribute to

the personalization of therapy and created new drugs

to target specific mechanisms. (Rosik 2020) What is

more, even though clinical genetic testing procedure

for GDM is not available yet, this review could offer

references for the range of genes considered in future

genetic testing of GDM.

2 THE CONCEPTION OF GDM

GDM is the type of diabetes developed or first

recognized during pregnancy, in which glucose

intolerance results in different severities of

hyperglycemia. The prevalence of GDM has a

noticeable rise during last decade. Because of its

obvious regional difference and inconsistency in the

diagnostic criteria, the prevalence of GDM is varied

all over the world, ranging from 1 to 20%. (Alfadhli

2015) There’s no sign that the trend will stop

worsening. Thus, inventing procedures for the

prevention, diagnosis, and treatment of GDM is of

urgency.

Typically, GDM doesn’t have overt symptoms.

Even at present, it’s difficult to distinguish them from

normal pregnancy symptoms, making a uniform and

sound screening for GDM crucial. However,

currently, a global standard strategy for screening and

diagnosing GDM is not available. The most adopted

diagnostic criterion is the one recommended by WHO

in 2013. An oral glucose tolerance test (OGTT) is

usually required for diagnosis, during which the

plasma glucose concentrations are measured

regularly after ingesting 75 grams of glucose.

Zheng, Q.

Genes Associated with Metabolism in Gestational Diabetes Mellitus: A New Categorization According to Risk Factors.

DOI: 10.5220/0011202300003443

In Proceedings of the 4th International Conference on Biomedical Engineering and Bioinformatics (ICBEB 2022), pages 295-302

ISBN: 978-989-758-595-1

295

(Alfadhli 2015) GDM is diagnosed when blood sugar

levels range from 92 mg/dl fasting, ≥180 mg/dl in the

60th minute of the OGTT, or ≥153 mg/dl in the 120th

minute of OGTT. (Rosik 2020) Once GDM is

diagnosed, lifestyle management like dietary changes

and physical exercise should be initiated. Long-term

self-monitoring of blood glucose levels is another

necessary part of GDM management. When these

interventions fail to control the patient’s blood

glucose level within an acceptable range, medical

therapies with insulin, glyburide, and metformin will

be introduced. (Alfadhli 2015) In general, GDM is a

disease requiring a lot of individual effort. This trait

leads to a relatively low-costing treatment for GDM

as well as some uncontrollability in its treatment

because of the huge variation in people’s self-

discipline.

Undiagnosed GDM increases the risk for

gestational hypertension, pre-eclampsia,

macrosomia, birth defects, and polyhydramnios.

(Metzger 2008) Also, evidence show that women

with GDM have six times higher possibility to

develop diabetes after pregnancy, which accordingly

makes GDM a significant predictor for T2DM

(Damm 2016). Furthermore, children of patients with

GDM are two to eight times more likely to develop

obesity, metabolic syndrome, and T2DM, compared

to children of mothers without GDM (Damm 2016).

If the worsening trend of GDM continues, a

widespread deterioration in blood glucose control is

in expectation due to the impact from generation to

generation.

Figure 1: A vicious cycle of obesity and diabetes from generation to generation (Alejandro 2020).

A vicious cycle of obesity and diabetes from

generation to generation exists. As evidence show,

obesity, a history of GDM and a strong family history

of diabetes are all strong factors leading to the

occurrence of GDM. Then, children of patients with

GDM are two to eight times more likely to develop

obesity and T2DM. However, there are still three

major windows of opportunities to take interventions

to break the vicious cycle. First, prevent risk factors

for GDM, like obesity and PCOS. Second, manage

and control blood sugar levels during pregnancy.

Third, control weight gain and prevent obesity during

adolescence. A break of the vicious cycle is critical as

the prevalence of diabetes including GDM continues

to increase over years.

3 THE NORMAL PREGNANCY

METABOLISM AND

PATHOPHYSIOLOGY OF GDM

During pregnancy, maternal metabolism changes

greatly to provide and store enough nutrients for

different stages of pregnancy. For instance, an

increased lipid storage often occurs. The key

metabolic alteration is a substantial increase in insulin

resistance. Placenta and other hormones, including

human placental lactogen, placental growth hormone,

progesterone, leptin, cortisol, prolactin, human

chorionic gonadotropin, and estradiol, are possible

factors leading to increased insulin resistance. [6-9]

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Other factors include tumor necrosis factor-alpha

(TNF-α) and other inflammatory mediators which are

produced by the placenta and other tissues. (Lowe

2014)

To compensate for the pregnancy-induced insulin

resistance, an enhancement in insulin secretion is

initiated. GDM develops when beta cells fail to

secrete as much insulin as needed in pregnancy.

Another possible mechanism leading to GDM is that

pregnancy-induced insulin resistance triggers a

genetic predisposition of impairment of beta cell

function. (Lambrinoudaki 2010)

Figure 2: Normal glucose-insulin metabolism during pregnancy. (Lizárraga 2021)

Part 1. The pancreas produces insulin which can

act on multiple organs, adipose tissue, and muscle to

control blood glucose levels. Part 2. Placenta and

other hormones, proinflammatory cytokines like

TNF-α cause increased insulin resistance/decreased

insulin sensitivity. Part 3&4. Then, there’s an

accordingly decrease in lipid storage and an increase

in lipolysis activity, resulting in higher amounts of

free fatty acids (FFA) in the bloodstream. Meanwhile,

promoted gluconeogenesis occurs in the liver and

depressed glycogen synthesis occurs in skeletal

muscles, leading to higher amounts of glucose in the

bloodstream. Part 5. FFA and glucose are transported

into the placenta to provide enough nutrients to

support fetal growth. Part 6. GDM develops when

factors like beta-cell dysfunction and genetics lead to

insufficient insulin secretion that fails to compensate

for the increased insulin resistance.

4 GENETICS IN VARIOUS RISK

FACTORS

Common GDM risk factors include a history of

GDM, a strong family history of diabetes, obesity,

PCOS, ethnicity, and advanced maternal age.

(Alfadhli 2015) Except for advanced maternal age,

other risk factors are all supported with genetic

evidence. This study aims to achieve a new

categorization of genes that have been identified to be

related to GDM according to their correspondence to

different risk factors. Because of an uneven

distribution of efforts in studies on these risk factors,

a ranking of the importance of the risk factors cannot

be safely concluded. Based on current evidence, a

history of GDM and a strong family history of

diabetes are the biggest risk factors. The following

ordering of risk factors is primarily according to the

amount of available evidence.

4.1 A History of GDM & a Strong

Family History of Diabetes

A history of GDM and a strong family history of

diabetes are two risk factors that share many

physiological traits. Previous studies achieved most

findings of genes responsible for glucose metabolism

and mechanisms regarding insulin like beta cell

function. It provides a sound explanation for why a

history of GDM and a strong family history of

diabetes, which contribute to progressive

dysfunctions of these mechanisms, are significant

risk factors of GDM. Interestingly, GDM has a

similar impact on the risk of T2DM.

4.1.1 Transcription Factor 7–like 2

(TCF7L2)

Many TCF7L2 polymorphisms have threatening

Genes Associated with Metabolism in Gestational Diabetes Mellitus: A New Categorization According to Risk Factors

297

effects on the risk of GDM. In their study, Zhang et

al. showed that rs7903146 (OR=1.44, p < 0.001) and

rs12255372 (OR=1.46, P = 0.002) are strongly

correlated with increased GDM risk. (Zhang 2021)

Reducing insulin secretion is the potential pathway.

Figure 3. Potential metabolic pathways that TCF7L2 may contribute to. (Chiang 2012).

TCF7L2 is an important protein involved in Wnt

signaling pathway by contributing to the formation of

a key effector in this pathway. Wnt pathway impacts

glucose homeostasis by controlling the gene

expression and functioning of some hormones, like

GLP-1, GIP, and insulin. This pathway also affects

adipogenesis negatively. Even though the mechanism

of how TCF7L2 polymorphisms leads to increased

GDM risk is unclear, this finding has been

successfully replicated several times. Insulin down-

regulates the gene expression of TCF7L2 in

adipocytes. People with insulin resistance are

observed to have higher levels of TCF7L2 in adipose

tissue.

4.1.2 Melatonin Receptor 1B (MTNR1B)

MTNR1B has the potential effect of antagonizing

insulin release. (Dalfrà 2020) Its polymorphism

rs10830963 was more frequently found in GDM

patients compared to the controls (48.4% vs. 42.3%).

(Zhang 2014) The G allele carriers were observed to

increase the risk of GDM risk (OR=1.24, p <

0.00001). (Zhang 2014) A similar association was

found between rs1387153 single-nucleotide

polymorphism (SNP) and GDM risk. (Zhang 2014)

4.1.3 Glucokinase (GCK)

GCK amplifies the secretion when a rise in blood

glucose is detected to manage insulin secretion.

(Dalfrà 2020) A meta-analysis of recent studies found

a significant association between rs1799884 and

enhanced GDM risk with an OR of 1.29 (p < 0.001).

(Zhang 2021)

4.1.4 Glucokinase Regulatory Protein

(GCKR)

GCKR encodes regulatory proteins that exert an

inhibiting effect on GCK in the liver and pancreatic

islet cells. (Dalfrà 2020) The polymorphism rs780094

C/T SNP was found to be associated with a decrease

in GDM risk in all populations in its dominance,

recessive, and allelic models. (Lin 2018)

4.1.5 Potassium Channel Inwardly

Rectifying Subfamily J member 11

(KCNJ11)

KCNJ11 contributes to the regulation of insulin

secretion. (Dalfrà 2020) A meta-analysis conducted

by Zhang et al. demonstrated a modest correlation

between KCNJ11 rs5219 (E23K) and increased GDM

risk (OR=1.15, P=0.002). (Zhang 2021)

4.1.6 CDK5 Regulatory Subunit

Associated Protein 1 Like 1

(CDKAL1)

CDKAL1 is involved in beta cell function and insulin

release. (Rosik 2020) Guo et al. demonstrated that

rs7754840 and rs7756992 were all significantly

correlated with GDM risk. (Guo 2018)

4.1.7 Solute Carrier Family 30, Member

8 (SLC30A8)

SLC30A8 is only expressed in the pancreas and is

responsible for insulin secretion. (Dalfrà 2020) In

their study, Lin et al. demonstrated a protective effect

of rs13266634 C/T SNP on GDM development. (Lin

2018)

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4.1.8 Insulin Receptor Substrate 1

(IRS1)

IRS1 encodes insulin receptor substrate 1 which

impacts insulin signaling. (Dalfrà 2020) A meta-

analysis determined that the T allele of IRS1

rs1801278 was discovered more frequently in GDM

patients than in the control group (8.7% vs. 5.1%). It

leads to the conclusion that rs1801278 is strongly

correlated with GDM risk. (Zhang 2014)

4.1.9 Insulin Like Growth Factor 2

MRNA Binding Protein 2

(IGF2BP2)

IGF2BP2 encodes proteins which play a role in

insulin secretion. Zhang et al. identified the effect of

rs4402960 on increasing GDM risk (OR=1.21,

P<0.001). (Zhang 2021)

Many of the recent studies were testing the

association between T2DM susceptibility genes with

GDM, based on the assumption that there is a shared

genetic architecture between GDM and T2DM.

(Lowe 2014) Also, there is a potential bias because

the references this review relies on only included a

relatively limited number of women with GDM,

compared to a large number of patients with T2DM

involved in these references. Among tested genes, the

nine genetic loci listed are demonstrated to be also

associated with GDM and are supported with the

most evidence. However, many T2DM susceptibility

genes failed to show any evidence for their

correlation with GDM. (Lowe 2014) Therefore,

although further examination of the association

between other T2DM susceptibility genes and GDM

is of great significance to understand GDM,

exploration of other underlying genetic mechanisms

is still necessary.

4.2 Obesity

As a rising public health crisis aggravated by the

widespread sedentary lifestyles, obesity leads to

many life-threatening diseases, including GDM. It is

shown that the prevalence of GDM in normal-weight

women, defined as women whose pre-pregnancy

BMI is 18.5-24.9, is 3.6%, whereas that in women

with BMI over 40.0 is 13.9%. (Deputy 2018) Some

genes related to lipid metabolism showed evidence

for association with increased GDM risk.

FTO contributes to regulating fat mass,

adipogenesis and body weight. FTO rs9939609 T/A

SNP had an identified association with increased

GDM risk. (Dalfrà 2020) Moreover, according to

Yang et al.’s study, there were associations between

FTO gene rs11075995, rs3826169, rs74245270,

rs74018601, rs7205009 and rs9888758 and the

enhancement of the risk of GDM. (Yang 2020)

Furthermore, an enhancement in the gene

expression of the adipokines TNFα, IL-1β and or

leptin was investigated to increase in adipose tissue

from obese and GDM women. On the other hand, the

gene expression of LPL, FATP2, FATP6, ASCL1,

PNPLA2, PPARδ, PPARγ and RXRα was observed

to decrease in GDM patients. (Lappas 2014)

As findings of the association between FTO and

GDM risk are inconsistent, further studies are in need

to determine the association. Still, the association

between other genes involved in lipid metabolism and

GDM risk is worth examination.

4.3 PCOS

PCOS is a health condition characterized by

hyperandrogenism, anovulation, and insulin

resistance. Similar to the relationship between T2DM

and GDM, PCOS may contribute to an increase of the

risk of GDM.

Fibroblast growth factor (FGF) 19 & 21. FGF 19

and FGF 21, encoding adipokines which are involved

in insulin resistance and serum levels of adiponectin,

were identified to be correlated with GDM risk.

(Wang 2013) Moreover, GDM patients with PCOS

history were observed to have much lower levels of

FGF 19 than GDM patients without PCOS history

and controls without PCOS history. (Wang 2013)

Wang et al. indicated that a decrease in serum FGF19

level was a possible part of the pathophysiology of

GDM. (Wang 2013) On the other hand, increased

serum FGF 21 was potentially involved in a

compensatory response to GDM. (Wang 2013)

Even though only limited genes are found to

prove the association between PCOS and GDM

genetically, evidence for such genes’ involvement in

the pathophysiology of GDM is encouraging,

providing potential research directions of the

pathophysiology of GDM.

4.4 Ethnicity

Many statistics show that the prevalence of GDM

varies among different ethnic groups, even though the

genetic evidence is not obvious yet. Non-Hispanic

Asian women had the highest prevalence (11.1%).

(Deputy 2018) In general, GDM has higher frequency

among African, Hispanic, Indian, and Asian women

than among Caucasian women. (Alfadhli 2015)

PPARG is a gene that demonstrated some association

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with ethnicity.

Peroxisome proliferator-activated receptor γ

(PPARG). PPARG plays a role in the regulation of

adipocyte differentiation and glucose homeostasis. Its

polymorphism rs1801282 was demonstrated to be

associated with GDM risk only in the Asian

population. However, the association was not

identified in the Caucasian population. (Metzger

2008)

The lack of genetic evidence for this association

could be because many previous studies contained

groups of mixed ancestry or participants from a single

ethnic group. Further findings of such genes require

research incorporating and comparing various ethnic

groups.

Figure 4: Age-adjusted prevalence of GDM by ethnicity and years: Northern California Kaiser Permanente, 1991-2000

(Ferrara 2007).

All ethnic groups were investigated to have a

similar increasing trend in their prevalence of GDM

in the Northern California Kaiser Permanente study

from 1991-2000. Asian and Hispanic women had

higher prevalence of GDM than other ethnic groups.

5 DISCUSSIONS

Clinical genetic testing is already available for

monogenic forms of diabetes, especially Maturity

Onset Diabetes of the Young (MODY). The

mutations of 14 genes are identified to be individually

associated with the occurrence of MODY. (Firdous

2018) Currently, the genetic diagnosis of MODY is

usually done through next-generation sequencing

(NGS), which analyzes the mutations in DNA

isolated from blood sample. This method usually

diagnoses MODY with almost 100% sensitivity.

(Firdous 2018)

However, in terms of polygenic forms of diabetes

like T2DM and GDM, the genetic etiology is much

more complicated. What further extends the difficulty

of clinical genetic testing for polygenic forms of

diabetes is the interactive impacts of environmental

and lifestyle factors on such diabetes.

Nonetheless, the application of genetic diagnosis of

MODY is still inspiring for the development of a

genetic diagnosis procedure of GDM in the future.

There could be a huge step forward if some genes

unique to the etiology of GDM are identified. This is

also in accordance with the aim of this review—to

provide insights on underlying mechanisms (other

than the shared genetic architecture with T2DM) that

have the potential to find genes associated with GDM

exclusively.

6 CONCLUSIONS

In this paper, through the combining of related

references, a new classification of genes that

influence GDM was made and based on their

correspondence to various risk factors. The

underlying mechanisms that are associated with

obesity (lipid metabolism), PCOS, and race were also

revealed in this paper. Finally, it was concluded that

except for advanced maternal age, other risk factors

such as history of GDM, a strong family history of

diabetes, obesity, PCOS, ethnicity were identified to

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have genetic evidence associated with an increase in

GDM risk. Among them, history of GDM and a

strong family of history combined (due to their

similarity in physiological mechanism) had the

highest number of genes with a demonstrated

association with GDM. This trend could be attributed

to the concentrated research on the similarity between

the genetic architecture of T2DM and GDM.

However, this paper could be further improved with

more research on obesity, PCOS, race and other

related genes, as well as an in-depth explanation of

their metabolic mechanisms. In the future, with more

samplings and diverse ethnic groups, more associated

genes may be found by related researchers and may

contributing to the genetic testing for GDM.

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