literature review on gestational diabetes pdf

• Search Menu
• Sign in through your institution
• Advance Articles
• Thematic Issues
• ENDO Meeting Abstracts
• Clinical Practice Guidelines
• Endocrine Reviews
• Endocrinology
• Journal of the Endocrine Society
• The Journal of Clinical Endocrinology & Metabolism
• JCEM Case Reports
• Molecular Endocrinology (Archives)
• Endocrine Society Journals
• Author Guidelines
• Submission Site
• Open Access
• Why Publish with the Endocrine Society?
• Advertising & Corporate Services
• Reprints, ePrints, Supplements
• About Endocrine Reviews
• Editorial Board
• Author Resources
• Reviewer Resources
• Rights & Permissions
• Member Access
• Terms and Conditions
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Current gdm diagnostic criteria, contemporary clinical evidence following the revised iadpsg gdm diagnostic criteria, current classification of hyperglycemia in pregnancy and gdm, the impact of preanalytical glucose processing standards on the diagnosis of gdm, incidence and prevalence of gdm, risk factors for gdm, pathophysiology of gdm, genetics of gdm, maturity-onset diabetes of the young, consequences of gdm, neonatal complications, short-term risk, long-term risk in the offspring, maternal complications, management of gdm, lifestyle intervention, gestational weight gain, maternal glucose targets, insulin therapy, oral pharmacotherapy, obstetric management, longer term management of women following gdm, treatment of gdm and long-term offspring outcomes, precision medicine in gdm: physiological heterogeneity, subtype classification, risk prediction, and biomarker utility, the covid-19 pandemic and gdm, financial support, disclosure summary, a clinical update on gestational diabetes mellitus.

• Article contents
• Figures & tables
• Supplementary Data

Arianne Sweeting, Jencia Wong, Helen R Murphy, Glynis P Ross, A Clinical Update on Gestational Diabetes Mellitus, Endocrine Reviews , Volume 43, Issue 5, October 2022, Pages 763–793, https://doi.org/10.1210/endrev/bnac003

• Permissions Icon Permissions

Gestational diabetes mellitus (GDM) traditionally refers to abnormal glucose tolerance with onset or first recognition during pregnancy. GDM has long been associated with obstetric and neonatal complications primarily relating to higher infant birthweight and is increasingly recognized as a risk factor for future maternal and offspring cardiometabolic disease. The prevalence of GDM continues to rise internationally due to epidemiological factors including the increase in background rates of obesity in women of reproductive age and rising maternal age and the implementation of the revised International Association of the Diabetes and Pregnancy Study Groups’ criteria and diagnostic procedures for GDM. The current lack of international consensus for the diagnosis of GDM reflects its complex historical evolution and pragmatic antenatal resource considerations given GDM is now 1 of the most common complications of pregnancy. Regardless, the contemporary clinical approach to GDM should be informed not only by its short-term complications but also by its longer term prognosis. Recent data demonstrate the effect of early in utero exposure to maternal hyperglycemia, with evidence for fetal overgrowth present prior to the traditional diagnosis of GDM from 24 weeks’ gestation, as well as the durable adverse impact of maternal hyperglycemia on child and adolescent metabolism. The major contribution of GDM to the global epidemic of intergenerational cardiometabolic disease highlights the importance of identifying GDM as an early risk factor for type 2 diabetes and cardiovascular disease, broadening the prevailing clinical approach to address longer term maternal and offspring complications following a diagnosis of GDM.

Gestational diabetes mellitus (GDM) is 1 of the most common medical complications of pregnancy and is increasing in prevalence globally.

GDM is associated with obstetric and neonatal complications primarily due to increased birthweight and is a major risk factor for future type 2 diabetes, obesity, and cardiovascular disease in mother and child.

Detecting GDM is important because perinatal complications and stillbirth risk are greatly reduced by treatment.

A precision medicine approach to GDM which recognizes severity and onset of maternal hyperglycemia as well as genetic and physiologic subtypes of GDM may address the current diagnostic controversy via accurate risk stratification and individualized treatment strategies, leading to improved clinical care models and outcomes.

The traditional focus on normalization of obstetric and neonatal outcomes achieved via short-term antenatal maternal glucose management should now shift to early postnatal prevention strategies to decrease the progression from GDM to type 2 diabetes and address longer term maternal and offspring metabolic risk given the global epidemic of diabetes, obesity, and cardiovascular disease.

Diabetes in pregnancy was first described in 1824 by Bennewitz in Germany ( 1 ), with subsequent case series in the United Kingdom and United States reporting high perinatal mortality rates in women with diabetes in pregnancy ( 2-4 ). In 1909, Williams reported arguably the first diagnostic criteria for diabetes in pregnancy in the United States, proposing physiological and pathophysiological thresholds for “transient glycosuria in pregnancy” ( 5 ).

In 1964, O’Sullivan and Mahan defined specific diagnostic criteria for gestational diabetes mellitus (GDM) in the United States derived from the 100-g 3-hour oral glucose tolerance test (OGTT) undertaken in the second and third trimester of pregnancy in 752 women ( 6 ). GDM was defined as ≥2 venous whole blood glucose values greater than 2 SD above the mean glucose values for pregnancy in their initial cohort. These glucose thresholds were primarily chosen because the resulting GDM prevalence of 2% corresponded to the background population prevalence of diabetes, while the requirement of ≥2 elevated glucose values sought to minimize the risk of preanalytical error ( 7 ). These thresholds were validated by their identification of subsequent diabetes up to 8 years postpartum in an additional cohort of 1013 women. Increased perinatal mortality was also observed in women with ≥2 glucose values exceeding the proposed diagnostic criteria ( 6 ). In 1965, the World Health Organization (WHO) concurrently recommended that GDM be diagnosed by either a 50- or 100-g OGTT using the 2-hour postload glucose value, but the threshold used was the same as for diagnosing diabetes in the nonpregnant population ( 8 ). The WHO continued to diagnose GDM based on glucose thresholds for diabetes in the nonpregnant population ( 9 , 10 ) until its endorsement of the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) diagnostic criteria in 2013 ( 11 ).

Since 1973, the screening approach to GDM frequently adopted a 2-step procedure with the 50-g 1-hour glucose challenge test (GCT) followed by the 100-g 3-hour OGTT if the GCT was positive. This was based on data from O’Sullivan et al, which showed that a 2-step diagnostic approach to GDM using the GCT as the initial screening test and a glucose threshold of 7.9 mmol/L (143 mg/dL) was 79% sensitive and 87% specific for diagnosing GDM on the 100-g 3-h OGTT in a cohort of 752 women ( 12 ). The rationale for this approach was the efficient identification of women most at risk of GDM.

In 1979, the US National Diabetes Data Group (NDDG) published conversions of the original O’Sullivan and Mahan 100-g 3-hour OGTT diagnostic criteria for GDM, reflecting the transition from venous whole blood glucose to plasma blood glucose analysis ( 13 ). These revised criteria were subsequently adopted by the American Diabetes Association (ADA) and internationally ( 9 , 14 , 15 ). In 1982, Carpenter and Coustan recommended lowering of the NDDG diagnostic criteria, reflecting newer preanalytical enzymatic methods that were more specific for plasma glucose ( 7 , 16 ). They also advised lowering the GCT glucose threshold to 7.5 mmol/L (135 mg/dL) based on their study of 381 women who underwent the 100-g 3-h OGTT after screening positive on the GCT, whereby a GCT glucose threshold ≤ 7.5 mmol/L (135 mg/dL) strongly correlated with a normal OGTT ( 17 ). However, in the absence of clear evidence supporting a specific glucose threshold for the GCT, the ADA and the American College of Obstetricians and Gynecologists (ACOG) continued to recommend a screen positive GCT glucose threshold from 7.2 to 7.8 mmol/L (130-140 mg/dL) for GDM ( 18 , 19 ).

The ADA did however recommend the modified Carpenter and Coustan diagnostic glucose thresholds for GDM from 2000 ( 20 ), supported by the findings of the Toronto Tri-Hospital Gestational Diabetes Project ( 21 , 22 ). These data demonstrated a positive correlation between increasing maternal hyperglycemia even below the NDDG diagnostic criteria for GDM and risk of obstetric and neonatal complications including preeclampsia, cesarean section, and macrosomia (neonatal birthweight > 4000 g) ( 21 , 22 ). In addition, several large cohort studies showed that women diagnosed (but not treated) with GDM based on the Carpenter and Coustan criteria were at increased risk of perinatal complications including hypertensive disorders of pregnancy, increased birthweight, macrosomia, neonatal hypoglycemia, hyperbilirubinemia, and shoulder dystocia, compared to women diagnosed and treated as GDM by NDDG diagnostic criteria ( 16 , 23-25 ). From 2003 the ADA additionally endorsed the 1-step 75-g 2-hour OGTT for the diagnosis of GDM derived from the modified Carpenter and Coustan fasting, 1- and 2-hour glucose thresholds for the 100-g 3-hour OGTT, particularly for women at high-risk ( 26 ). This approach was deemed more cost-effective, albeit less validated, than the 100-g 3-hour OGTT. The use of the modified Carpenter and Coustan thresholds was associated with an almost 50% increase in prevalence of GDM ( 16 , 23 ).

The evolution of diagnostic criteria for GDM illustrates the historic lack of consensus for the diagnosis of GDM, with the presence or absence of disease varying dependent on expert consensus. The underlying rationale for the diagnosis of GDM also shifted over time toward identifying perinatal risk rather than future maternal diabetes risk.

The seminal Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study sought to provide an evidence base to guide risk in GDM, and its results were published in 2008 ( 27 ). This large, international, prospective, observational study evaluated the relationship between glucose levels on the 75-g 2-hour OGTT performed at 24 to 32 weeks’ gestation (mean 27.8 weeks’ gestation) in over 25 000 pregnant women with the following primary perinatal outcomes: birthweight > 90th percentile for gestational age, primary cesarean section delivery, neonatal hypoglycemia, and cord blood serum C-peptide > 90th centile. Secondary outcomes were preeclampsia, preterm delivery (defined as delivery before 37 weeks’ gestation), shoulder dystocia or birth injury, hyperbilirubinemia, and neonatal intensive care admission. The results showed a continuous positive linear relationship between maternal fasting; 1- and 2-hour plasma glucose levels obtained on the OGTT, below those that were diagnostic of diabetes outside pregnancy; and risk of primary outcomes ( 27 ). Notably, there were no specific glucose thresholds at which obstetric and neonatal complications significantly increased.

Based on these findings and supported by trials [the Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) and the Maternal-Fetal Medicine Units Network (MFMU) trial] showing benefit of treatment of more severe and “mild” degrees of maternal hyperglycemia, respectively ( 28 , 29 ), the IADPSG revised its diagnostic criteria for GDM. Despite the lack of a clear diagnostic glucose threshold in HAPO, the consensus of the IADPSG was to define diagnostic thresholds for the fasting, 1- and 2-hour glucose values for the 75-g 2-hour OGTT based on the average glucose values at which the odds of the primary outcomes were 1.75 times the odds of these outcomes occurring at the mean glucose levels for the HAPO cohort ( 30 ). The IADPSG consensus was also that only 1 elevated glucose level for the OGTT was required for GDM diagnosis, as each glucose threshold represented broadly comparable level of risk. Thus, the main purpose of the diagnostic criteria for GDM post-HAPO was to define the level of risk associated with increased perinatal complications.

Post-HAPO, there exist several different screening and testing approaches for the diagnosis of GDM internationally. The IADPSG and WHO recommend universal testing of all pregnant women between 24 to 28 weeks’ gestation with the 75-g 2-hour OGTT ( 11 , 30 ). These revised recommendations were largely endorsed by several organizations including the ADA ( 18 ), Endocrine Society ( 31 ), International Federation of Gynecology and Obstetrics ( 32 ), Australasian Diabetes in Pregnancy Association ( 33 ), Japan Diabetes Society ( 34 ), Ministry of Health of China ( 35 ), and the European Board of Gynecology and Obstetrics ( 36 ).

The National Institutes of Health did not endorse the IADPSG recommendations, citing the expected increase in prevalence of GDM, cost, and intervention in the context of a lack of evidence for an associated improvement in perinatal outcomes ( 37 ). The National Institutes of Health and ACOG continue to recommend a 2-step testing approach, with the initial screening GCT for all women and those who screen positive proceeding to the diagnostic 100-g 3-hour OGTT ( 19 , 37 ). This approach is also endorsed by ADA ( 18 ). However, the ACOG’s 2018 guidelines now acknowledge that individual practices and institutions may instead choose to use the IADPSG’s 1-step testing approach and diagnostic criteria if appropriate for their population ( 19 ). The UK National Institute for Health and Care Excellence (NICE) guidelines advise a selective screening approach, whereby women with risk factors for GDM are recommended to undergo a diagnostic 75-g 2-hour OGTT at 26 to 28 weeks’ gestation, with diagnostic (fasting or 2-hour) glucose thresholds higher than the IADPSG diagnostic criteria for GDM ( 38 ). Several other European bodies also currently recommend selective risk factor-based screening, with only women fulfilling specific high-risk criteria proceeding to a diagnostic OGTT, even if the IADPSG diagnostic criteria for GDM are applied ( 39 , 40 ). The revised IADPSG diagnostic criteria and testing approach to GDM in comparison to other international organizations are summarized in Table 1 .

Current international testing approach to gestational diabetes mellitus

Abbreviations: ACOG, American College of Obstetricians and Gynecologists; ADA, American Diabetes Association; ADIPS, Australasian Diabetes in Pregnancy Association; CDA, Canadian Diabetes Association; CNGOF, Organisme professionnel des médecins exerçant la gynécologie et l'obstétrique en France; DDG, German Diabetes Association; DGGG, European Board of Gynecology and Obstetrics; DIPSI, Diabetes in Pregnancy Study Group of India; FIGO, International Federation of Gynecology and Obstetrics; GCT, glucose challenge test; IADPSG, International Association of the Diabetes and Pregnancy Study Groups; JDS, Japan Diabetes Society; NDDG, US National Diabetes Data Group; NICE, National Institute for Health and Care Excellence; OGTT, oral glucose tolerance test; WHO, World Health Organization.

a The ADA states that the choice of a specific positive GCT screening threshold is based upon the trade-off between sensitivity and specificity ( 41 ). ACOG advises that in the absence of clear evidence that supports a specific GCT threshold value between 7.2 and 7.8 mmol/L, obstetricians and obstetric care providers may select a single consistent GCT threshold for their practice based on factors such as community prevalence rates of GDM ( 19 ).

b Plasma or serum glucose.

c ACOG 2018 Clinical Practice Bulletin on GDM continues to recommend 2-step testing for GDM but states that individual practices and institutions may choose to use the IADPSG’s 1-step testing approach and diagnostic criteria if appropriate for their population ( 19 ).

d ACOG 2018 Clinical Practice Bulletin on GDM acknowledges that women who have even 1 abnormal value on the 100-g 3-hour OGTT have a significantly increased risk of adverse perinatal outcomes compared to women without GDM but state that further research is needed to clarify the risk of adverse outcomes and benefits of treatment in these women ( 19 ).

e A glucose level ≥ 11.1 mmol/L following the initial screening GCT is classified as GDM, and there is no need for a subsequent 2-hour 75-g OGTT.

f BMI > 30 kg/m 2 , previous macrosomia (≥4500 g), previous GDM, family history of diabetes, and family origin with a high prevalence of diabetes (South Asian, Black Caribbean, Middle Eastern) ( 38 ).

g Maternal age ≥ 35 years, body mass index ≥ 25 kg/m 2 , family history of diabetes, previous GDM, previous macrosomia ( 39 ).

h If first trimester fasting glucose normal (ie, < 5.1 mmol/L).

i Adapted from the WHO 1999 diagnostic criteria for GDM ( 45 ), using a nonfasting 75-g 2-hour OGTT ( 44 ).

It is important to consider the increase in GDM prevalence associated with the IADPSG diagnostic criteria in the context of the rising background rates of impaired glucose tolerance, type 2 diabetes, and obesity among young adults and women of reproductive age ( 46 , 47 ). For example, almost 18% of HAPO study participants would have met the IADPSG diagnostic thresholds for GDM. By comparison, the rate of prediabetes in US adults aged between 20 and 44 years is >29% ( 48 , 49 ).

Studies in Indian, Israeli, and US cohorts have suggested that the IADPSG testing approach and intervention for GDM is cost-effective based on a combination of delaying future type 2 diabetes and preventing perinatal complications ( 50-53 ). For example, a US study found that the IADPSG diagnostic criteria would be cost-effective if associated intervention decreased the absolute incidence of preeclampsia by >0.55% and cesarean delivery by >2.7% ( 53 ). In contrast, UK health economic data show that routinely identifying GDM is not cost-effective based on perinatal outcomes ( 54 ) and that the universal WHO (IADPSG) testing approach is less cost-effective than the NICE selective screening approach ( 55 ).

The lack of randomized controlled trials (RCTs) evaluating outcomes in women diagnosed with GDM based on the IADPSG criteria and the clinical relevance of treating the resulting milder degrees of hyperglycemia remain controversial ( 56 ). Several retrospective studies have shown that women diagnosed with GDM by the IADPSG criteria but who were previously classified as having normal glucose tolerance were still at increased risk for obstetric and neonatal complications, including gestational hypertension, preeclampsia, cesarean delivery, macrosomia, large-for-gestational-age (LGA), shoulder dystocia, and neonatal intensive care admission, compared to women with normal glucose tolerance ( 57-59 ). For example, a 2015 retrospective study in Taiwan comparing pregnancy outcomes in women diagnosed and treated for GDM using the 2-step (GCT followed by the 100-g 3-hour OGTT) approach compared to the IADPSG 1-step approach found that the latter was associated with a reduction in gestational weight gain (GWG), birthweight, macrosomia, and LGA ( 60 ). Another retrospective study in the United Kingdom reported that women who were diagnosed with GDM based on modified IADPSG diagnostic glucose thresholds but who screened negative for GDM on 2015 NICE diagnostic criteria had a higher risk of LGA, cesarean delivery, and polyhydramnios ( 61 ). Other retrospective studies have also demonstrated higher birthweight, birthweight z-score, ponderal index, and increased rates of LGA and cesarean delivery in untreated women diagnosed with GDM based on the IADPSG criteria, compared to women with normal glucose tolerance ( 62 , 63 ).

The recent randomized ScreenR2GDM trial compared 1-step screening (75-g 2-hour OGTT) with 2-step screening (2 GCT thresholds ≥7.2 mmol/L and ≥7.8 mmol/L used, followed by the 100-g 3-hour OGTT) in 23 792 pregnant women in the United States ( 64 ). Despite doubling the diagnosis of GDM with the 1-step approach (16.5% vs 8.5%), there were no differences in pregnancy complications including LGA [relative risk (RR) 0.95; 97.5% CI 0.87-1.05], perinatal composite outcome (RR 1.04; 97.5% CI 0.88-1.23), gestational hypertension or preeclampsia (RR 1.00; 97.5% CI 0.93-1.08), and primary cesarean section (RR 0.98; 97.5% CI 0.93-1.02) between the different screening approaches. These findings have not resolved the diagnostic debate for GDM, with some arguing that the 1-step approach therefore demonstrates insufficient perinatal benefit for the associated increased healthcare costs ( 65 ), while others have identified potential limitations in study methodology ( 7 , 47 , 65 , 66 ). Despite randomization to either testing strategy, the pragmatic trial design allowed clinicians to select a preferred strategy. Consequently, one third of women randomized to the 1-step approach did not adhere to the assigned screening and were tested via the 2-step approach, compared to only 8% of women randomized to the 2-step approach. Although the study attempted to adjust for this difference using inverse probability weighting, residual provider bias cannot be excluded ( 47 ). Given this was a population level analysis of GDM screening, GDM (treatment) status differed only for the 8% of women not diagnosed with GDM based on the 2-step approach who may have otherwise been diagnosed with GDM based on the 1-step approach. Whether these women had potentially worse outcomes that may have been mitigated by treatment cannot be determined by this study. However, given the rates of pharmacotherapy were similar between the 1- and 2-step cohorts at 43% and 46%, respectively ( 64 ), this strategy detected women with essentially an equivalent risk of hyperglycemia warranting pharmacotherapy ( 47 ). This observation is consistent with other studies in UK cohorts comparing the IADPSG testing approach to the less sensitive NICE and Canadian criteria, whereby women demonstrated insulin resistance and required pharmacotherapy for control of hyperglycemia even at the most sensitive thresholds of the IADPSG diagnostic criteria ( 67 ).

More generally, the GCT fails to detect approximately 20% to 25% of women with GDM, particularly those diagnosed with GDM based on an elevated fasting glucose ( 68 ). The frequency of GDM diagnosed by the OGTT fasting glucose threshold in the HAPO study ranged from 24% to 26% in Thailand and Hong Kong to >70% in the United States ( 69 ). This highlights the variability and thus limitations of post-glucose load screening based on ethnicity. Moreover, a recent systematic review and meta-analysis of 25 studies (n = 4466 women) showed that even 1 abnormal value on the diagnostic 3-hour 100-g OGTT is associated with an increased risk of perinatal complications compared to women with a normal GCT, and this risk was similar to that of women actually diagnosed with GDM ( 70 ).

The degree of benefit of treating women with GDM defined by the IADPSG diagnostic criteria is yet to be determined. The potential benefit is inferred from the treatment of maternal hyperglycemia described in the ACHOIS and MFMU intervention trials ( 28 , 29 ), whereby maternal glucose levels overlapped with the thresholds recommended by the IADPSG. It is worth noting that there are differences in these 2 trials with regards to the diagnostic criteria used to define GDM and cohort characteristics (eg, women were excluded from the MFMU trial if they had an abnormal glucose screening test prior to 24 weeks’ gestation or previous GDM), and thus the generalizability of these findings in women diagnosed with GDM based on the IADPSG criteria remains contentious.

The WHO first defined GDM in 1965 as “hyperglycemia of diabetic levels occurring during pregnancy” ( 8 ). Thus, historically, the term “GDM” encompassed the entire spectrum of maternal hyperglycemia in pregnancy, from pregestational diabetes to hyperglycemia first detected in pregnancy. In 1979, the NDDG defined GDM as “glucose intolerance that has its onset or recognition during pregnancy” ( 13 ). This was subsequently modified in 1985 at the Second International Workshop-Conference on Gestational Diabetes as “carbohydrate intolerance resulting in hyperglycemia of variable severity with onset or first recognition during pregnancy” and remained the most widely used definition of GDM until recently ( 71 ).

Contemporary nomenclature and diagnostic criteria now more clearly differentiate between women with pregestational diabetes and those with hyperglycemia first detected in pregnancy ( 30 ) ( Fig. 1 ). Pregestational diabetes includes type 1 diabetes, type 2 diabetes, and other types of diabetes such as cystic fibrosis-related diabetes, steroid/medication-induced diabetes, and monogenic diabetes.

Flowchart summarizing the contemporary nomenclature for hyperglycemia in pregnancy.

Hyperglycemia in pregnancy is now subclassified by the IADPSG into 2 separate categories, namely “overt diabetes mellitus during pregnancy” (overt diabetes) and GDM ( 30 ). Similarly, the WHO has a binary definition of hyperglycemia in pregnancy but has replaced the term “overt diabetes” with “diabetes mellitus in pregnancy” (DIP) ( 11 ). The rationale for the IADPSG recommendation for early testing in high-risk women is to diagnose DIP early in pregnancy. This is because DIP, diagnosed based on nonpregnant diabetes glucose thresholds, recognizes the increasing prevalence of undiagnosed preexisting diabetes in women of childbearing age as well as the greater risk associated with this degree of hyperglycemia ( 72-74 ). For example, a recent study in almost 5000 women in France found that DIP was associated with a 3.5-fold greater risk of hypertensive disorders in pregnancy compared to women with normal glucose tolerance, while early‐diagnosed DIP was associated with an increased risk of congenital malformation (7.7% vs 1.0% for women with normal glucose tolerance), suggesting that early hyperglycemia in pregnancy may sometimes be present at conception ( 75 ). However, DIP is not synonymous with preexisting diabetes. In Australian, women with DIP who performed an OGTT at 6 to 8 weeks postpartum, 21% had diabetes, 38% had impaired fasting glucose or impaired glucose tolerance, and 41% returned to normal glucose tolerance ( 76 ).

Regardless of the specific nomenclature used, DIP is distinct from GDM, which is defined by lower glucose thresholds on the OGTT and was historically considered to be a condition of mid to late pregnancy. The ADA has not accepted this nomenclature and defines GDM based on timing of diagnosis: women diagnosed with diabetes in the first trimester are classified as having (preexisting) type 2 diabetes, while GDM is defined as diabetes diagnosed in later pregnancy and not meeting the diagnostic criteria for type 2 diabetes ( 18 ). A summary of the current international nomenclature and diagnostic criteria for hyperglycemia in pregnancy is presented in Table 2 .

Classification and diagnostic criteria for hyperglycemia in pregnancy

75-g 2-hour OGTT: only 1 plasma glucose level needs to be elevated for the diagnosis of GDM. 100 g 3-hour OGTT: at least 2 plasma glucose levels need to be elevated for the diagnosis of GDM.

Abbreviations: ADA, American Diabetes Association; ADIPS, Australasian Diabetes in Pregnancy Association; EBCOG, European Board & College of Obstetrics and Gynaecology; FIGO, International Federation of Gynecology and Obstetrics; GCT, glucose challenge test; HbA1c, hemoglobulin A1c; IADPSG/; International Association of the Diabetes and Pregnancy Study Groups; GDM, gestational diabetes mellitus; OGTT, oral glucose tolerance test; WHO, World Health Organization.

a The IADPSG recommends confirmation by fasting plasma glucose or HbA1c for the diagnosis of overt diabetes during pregnancy ( 30 ).

Most international guidelines now recommend early antenatal testing for women at high risk to identify women with DIP ( 11 , 18 , 30 , 38 , 39 , 42-44 ). This has resulted in increased detection of milder degrees of hyperglycemia below the threshold of DIP, referred to as GDM diagnosed prior to 24 weeks’ gestation or early GDM. Studies in women with GDM have reported that between 27% and 66% of GDM can be detected in early pregnancy depending on the population as well as the screening and diagnostic criteria used ( 77-81 ).

Recent studies evaluating the relationship between maternal glycemia and fetal growth trajectories confirm the early impact of maternal glycemia on excess fetal growth and adiposity prior to the diagnosis of standard GDM from 24 weeks’ gestation. A US multiethnic prospective cohort study of 2458 women enrolled between 8 and 13 weeks’ gestation included 107 (4.4%) women with GDM ( 82 ). GDM was associated with an increase in estimated fetal weight from 20 weeks’ gestation, which became significant at 28 weeks’ gestation. Similarly, Sovio et al showed that excessive fetal growth occurred between 20 to 28 weeks’ gestation, prior to the diagnosis of GDM, especially among women with higher body mass index [BMI (kg/m 2 )] ( 83 ). An Indian study also showed that excess subcutaneous abdominal adiposity was first detected at 20 weeks’ gestation, at least 4 weeks prior to the diagnosis of GDM ( 84 ). Early excess adiposity persisted despite adjustments for maternal age, BMI, GWG, fetal sex, and gestational age and remained higher at 32 weeks’ gestation ( 84 ).

Currently, there is no consensus for the preferred testing approach or diagnostic glycemic thresholds for early GDM. The IADPSG recommends diagnosing early GDM based on a fasting glucose of 5.1 mmol/L to 6.9 mmol/L (92-124 mg/dL) ( 30 ), consistent with the diagnostic fasting glucose threshold for standard GDM. The utility of a single fasting glucose measurement for early GDM diagnosis warrants consideration. First, preanalytical glucose handling variation, particularly in the setting of a single glucose measurement, is a major issue for GDM diagnostic accuracy (discussed in the following text). Second, an Israeli cohort study of 6129 women who underwent a fasting glucose test at a median of 9.5 weeks’ gestation demonstrated a positive association between first trimester fasting glucose up to 5.8 mmol/L (104.5 mg/dL) and increased risk for subsequent diagnosis of GDM, LGA, macrosomia, and cesarean section ( 85 ). Similar to the HAPO study, a clear glucose threshold was lacking, with pregnancy complications evident at fasting glucose levels <5.1 mmol/L (92 mg/dL). Third, maternal fasting glucose decreases in the first trimester, most pronounced between 6 to 10 weeks’ gestation [median decrease in glucose 0.11 mmol/L (1.98 mg/dL)] ( 86 ), while studies have consistently shown that early fasting glucose is poorly predictive of GDM at 24 to 28 weeks’ gestation ( 86-88 ), leading to potential overdiagnosis of GDM. In China, an early fasting glucose between 6.1 mmol/L to 6.9 mmol/L (110-124 mg/dL) best corresponded to later GDM diagnosis ( 88 ), but this requires further validation.

The WHO recommends the same diagnostic OGTT glucose thresholds for GDM in early pregnancy as those derived from HAPO by the IADPSG ( 11 ). However, the prognostic value of these glucose levels in early pregnancy is yet to be established. Others have proposed an hemoglobin A1c (HbA1c) risk threshold ( 89 ), based primarily on evidence that an early HbA1c ≥ 5.9% (41 mmol/mol) detected all cases of DIP and predicted adverse pregnancy outcomes in a New Zealand cohort ( 90 ). However, studies in other cohorts have found that while an elevated HbA1c in early pregnancy is highly specific, it lacks sensitivity for identifying hyperglycemia and certain perinatal complications ( 91 , 92 ), with no clear benefit of treating women with HbA1c 5.7% to 6.4% (39-46 mmol/mol) in early pregnancy ( 93 , 94 ). A summary of the various international criteria for testing of GDM in early pregnancy is presented in Table 3 .

International criteria for testing of gestational diabetes mellitus in early pregnancy

75-g 2-h OGTT: Only 1 abnormal glucose level needs to be elevated for the diagnosis of GDM. 100-g 3-h OGTT: 2 abnormal glucose levels need to be elevated for the diagnosis of GDM.

Abbreviations: ADA, American Diabetes Association; ACOG, American College of Obstetricians and Gynecologists; ADIPS, Australasian Diabetes in Pregnancy Association; CNGOF, Organisme professionnel des médecins exerçant la gynécologie et l'obstétrique en France; DDG, German Diabetes Association; DGGG, European Board of Gynecology and Obstetrics; DIPSI, Diabetes in Pregnancy Study Group of India; EBCOG, European Board & College of Obstetrics and Gynaecology; GCT, glucose challenge test; GDM, gestational diabetes mellitus; IADPSG, International Association of the Diabetes and Pregnancy Study Groups; NICE, National Institute for Health and Care Excellence; OGTT, oral glucose tolerance test; WHO, World Health Organization.

a High-risk criteria not explicitly defined.

b IADPSG does not recommend routinely performing the 75-g 2-h OGTT prior to 24 weeks’ gestation but advises that a fasting glucose ≥ 5.1 mmol/L in early pregnancy be classified as GDM ( 30 ).

c GDM diagnosed at any time in pregnancy based on an abnormal 75-g 2-h OGTT ( 11 ).

d High-risk criteria defined as previous hyperglycemia in pregnancy; previously elevated blood glucose level; maternal age ≥ 40 years; ethnicity: Asian, Indian subcontinent, Aboriginal, Torres Strait Islander, Pacific Islander, Maori, Middle Eastern, non-White African; family history of diabetes (first-degree relative with diabetes or sister with hyperglycemia in pregnancy); prepregnancy body mass index > 30 kg/m 2 ; previous macrosomia (birth weight > 4500 g or > 90th percentile); polycystic ovary syndrome; and medications: corticosteroids, antipsychotics ( 33 ).

e High-risk criteria defined as body mass index ≥ 25 kg/m 2 (≥ 23 kg/m 2 in Asian Americans) plus 1 of the following: physical inactivity; previous GDM; previous macrosomia (≥ 4000 g); previous stillbirth; hypertension; high density lipoprotein cholesterol ≤ 0.90 mmol/L; fasting triglycerides ≥ 2.82 mmol/L; polycystic ovary syndrome; acanthosis nigricans; nonalcoholic steatohepatitis; morbid obesity and other conditions associated with insulin resistance; hemoglobulin A1c ≥ 5.7%; impaired glucose tolerance or impaired fasting glucose; cardiovascular disease; family history of diabetes (first-degree relative); and ethnicity: African American, American Indian, Asian American, Hispanic, Latina, or Pacific Islander ethnicity. Note that the ADA recommends testing for GDM at 24 to 28 weeks’ gestation and have no specific definition for early GDM ( 41 ).

f ACOG states that the best test for early GDM screening is not clear but suggest the testing approach and diagnostic criteria used to diagnose type 2 diabetes in the nonpregnant population and thus have no specific definition for early GDM ( 19 ).

g High-risk criteria defined as previous GDM; overweight/obesity; family history of diabetes (first-degree relative with diabetes); previous macrosomia (>4000g or >90th percentile); polycystic ovary syndrome; ethnicity: Mediterranean, South Asian, black African, North African, Caribbean, Middle Eastern, or Hispanic ( 36 ).

h High-risk criteria defined as age ≥ 45 years; prepregnancy body mass index ≥ 30 kg/m 2 ; physical inactivity; family history of diabetes; high-risk ethnicity (eg. Asians, Latin Americans); previous macrosomia ≥ 4500 g; previous GDM; hypertension; prepregnancy dyslipidemia (high-density lipoprotein cholesterol ≤ 0.90 mmol/L, fasting triglycerides ≥ 2.82 mmol/L); polycystic ovary syndrome; prediabetes in an earlier test; other clinical conditions associated with insulin resistance (eg, acanthosis nigricans); history of coronary artery disease/peripheral artery disease/cerebral vascular disease; medications associated with hyperglycemia (eg. glucocorticoids). Note that the DDG/DGGG recommends that a 75-g 2-h OGTT be the initial early test in high-risk women (defined as women with ≥2 risk factors for GDM) ( 43 ).

i High-risk criteria are defined as previous GDM, previous impaired glucose tolerance, and/or obesity ( 39 ).

j High-risk criteria defined as body mass index> 30 kg/m 2 ; previous macrosomia (≥4500 g); previous GDM; family history of diabetes (first-degree relative with diabetes); minority ethnic family origin with a high prevalence of diabetes. The updated 2015 NICE guidelines state that women with previous GDM should undergo early self-monitoring of blood glucose or a 75-g 2-hour OGTT as soon as possible after booking (first or second trimester), and a repeat 75-g 2-hour OGTT at 24 to 28 weeks’ gestation if the initial OGTT was negative ( 38 ).

k 2-hour postload glucose measured on nonfasting 75-g OGTT ( 44 ).

Despite the lack of diagnostic clarity for early GDM, increasing evidence suggests that women with early GDM represent a high-risk cohort ( 81 ). Early studies also reported worse pregnancy outcomes and increased insulin resistance in early GDM ( 78 , 95-97 ) but were confounded by the inclusion of women with pregestational diabetes. The first large retrospective cohort study excluding women with DIP showed that women diagnosed and treated for early GDM, especially those diagnosed in the first trimester, were more insulin resistant and at significantly greater risk for obstetric and neonatal complications compared to women diagnosed and treated for GDM from 24 weeks’ gestation ( 81 ). Other studies have since confirmed these findings ( 98 , 99 ). Concerningly, an increased risk of perinatal mortality and congenital abnormalities has also been reported in the offspring of women with early GDM ( 75 , 78 , 95 , 96 ), with some data demonstrating that 5% of women with early GDM have abnormal fetal echocardiograms ( 97 ). A recent meta-analysis of 13 cohort studies showed greater perinatal mortality among women with early GDM (RR 3.58; 95% CI 1.91-6.71) compared to women with a later diagnosis of GDM despite treatment ( 100 ).

A recent study assessing the pathophysiological characteristics of women diagnosed with GDM at a median of 16 weeks’ gestation compared to those diagnosed from 24 weeks’ gestation using IADPSG diagnostic criteria reported that women with early GDM had lower insulin sensitivity (defined by insulin-mediated glucose clearance during an OGTT), even after accounting for maternal BMI ( 101 ). Consistent with the pathophysiology of GDM, women with both early and standard GDM demonstrated impairment in pancreatic β-cell function ( 102 ). These data underscore GDM phenotypic differences, specifically based on timing of diagnosis and degree of hyperglycemia ( 103 ).

A key issue is the current lack of high-quality evidence that diagnosing and treating early GDM improves pregnancy outcomes. A recent major RCT in the United States evaluating early testing for GDM in 962 women with obesity included a subgroup analysis of women diagnosed and treated for GDM [early n = 69 (15.0%) vs standard n = 56 (12.1%)] based on the 2-step testing approach ( 104 ). The average gestational age at GDM diagnosis was similar at 24.3 ± 5.2 weeks for the early screen group compared to 27.1 ± 1.7 weeks in the routine screen group. There was no difference in pregnancy outcomes, although the primary composite perinatal outcome (macrosomia, primary cesarean delivery, gestational hypertension, preeclampsia, hyperbilirubinemia, shoulder dystocia, and neonatal hypoglycemia) was nonsignificantly higher in the early-screen group (56.9% vs 50.8%; P  = 0.06). Requirement for insulin therapy was almost 4-fold higher, while gestational age at delivery was lower (36.7 vs 38.7 weeks’ gestation; P  = 0.001) in women with early GDM. In a post hoc analysis of the Lifestyle in Pregnancy study ( 105 ), no difference in pregnancy outcomes was shown between women randomized to either lifestyle intervention (n = 36) or standard treatment (n = 54) in early pregnancy. Whether different glycemic targets are required reflecting physiological differences in early maternal glucose or whether additional risk factors contributing to a more insulin resistant phenotype such as maternal adiposity might also have a role remain unanswered ( 81 ). The ongoing Treatment of Booking Gestational Diabetes Mellitus study, evaluating the impact of immediate vs delayed care for gestational diabetes diagnosed at booking, will seek to determine whether or not there is benefit from treating early GDM ( 106 ).

Although the contemporary testing approach to GDM remains contentious, it is important to recognize that the diagnosis of GDM is based on the laboratory measurement of maternal glucose rather than a clinical diagnosis. Arguably then, a major issue in the contemporary diagnosis of GDM is optimizing preanalytical processing and measurement of maternal plasma glucose to ensure diagnostic accuracy ( 107 , 108 ). This includes optimization of sample handling and minimization of any analytic error. Unfortunately, stringent preanalytical processing standards are not currently routinely applied. The American Association for Clinical Chemistry (AACC) and ADA recommendations on laboratory testing in diabetes advise collection of plasma glucose in sodium fluoride tubes, with immediate placement in an ice slurry and centrifugation within 30 minutes ( 109 ). Citrate tubes are recommended as an alternative where early centrifugation is not possible. These standards are important because a major source of preanalytical glucose measurement error in sodium fluoride tubes is glycolysis by erythrocytes and leukocytes, which at room temperature lowers glucose levels prior to centrifugation at a rate of 5% to 7% per hour [~0.6 mmol/L (10 mg/dL)] ( 109 , 110 ). By 1 hour, this degree of glucose lowering is higher than the total analytical error threshold for glucose based on biological variation ( 107 ).

Recent studies have shown that OGTT preanalytical glucose processing variability greatly impacts the prevalence of GDM ( 67 , 111 ). Implementation of the AACC/ADA recommendations in a UK cohort resulted in higher mean glucose concentrations and 2.7-fold increased detection of GDM based on IADPSG criteria compared with the standard practice of storing sodium fluoride tubes at room temperature and delaying centrifugation until collection of all 3 OGTT samples ( 112 ). This increase in GDM diagnosis was entirely attributable to control of glycolysis ( 107 ). Similarly, in a large Australian multiethnic cohort (n = 12317), the rate of GDM diagnosis based on IADPSG criteria increased from 11.6% to 20.6% with early (within 10 minutes) vs delayed centrifugation ( 111 ). Mean glucose concentrations for the fasting, 1-hour, and 2-hour OGTT samples were 0.24 mmol/L (5.4%), 0.34 mmol/L (4.9%), and 0.16 mmol/L (2.3%) higher with early centrifugation, with the increase in GDM diagnosis primarily due to the resulting increase in fasting glucose levels ( 111 ). Importantly, the HAPO study, upon which the IADPSG diagnostic criteria for GDM was based, followed these AACC/ADA preanalytical glucose processing standards ( 111 ).

GDM is 1 of the most common medical complications of pregnancy ( 73 ). In 2019, the International Diabetes Federation (IDF) estimated that 1 in 6 live births worldwide were complicated by GDM ( 113 ). More than 90% of cases of hyperglycemia in pregnancy occur in low- and middle-income countries ( 114 ), where the prevalence and severity of maternal and neonatal complications associated with GDM ( 47 , 113 ) contrast with the near-normal pregnancy outcomes of modern management of GDM in developed countries ( 115 ).

The prevalence of GDM varies widely, depending on the population, the specific screening and the diagnostic criteria utilized. A 2012 systematic review of the diagnostic criteria used to define GDM reported a worldwide prevalence of GDM of 2% to 24.5% for the WHO criteria, 3.6% to 38% for the Carpenter and Coustan criteria, 1.4 to 50% for the NDDG criteria, and 2% to 19% for the IADPSG criteria ( 116 ).

Regardless of the specific diagnostic criteria or population, the prevalence of GDM continues to rise internationally, corresponding to epidemiological factors including the background rates of type 2 diabetes and increased incidence of obesity in women of childbearing age and rising maternal age ( 117-124 ). Implementation of the revised IADPSG diagnostic criteria have further increased the proportion of women being diagnosed with GDM ( 69 , 125 , 126 ). The incidence of GDM in the original HAPO study cohort applying the IADPSG diagnostic criteria ranged from 9.3% to 25.5% depending on study site ( 69 ). Recent international prevalence data also demonstrate marked variability in the rate of GDM, ranging from 6.6% in Japan and Nepal to 45.3% of pregnancies in the United Arab Emirates ( 127 ).

Several modifiable and nonmodifiable risk factors for GDM have been identified ( Table 4 ). A history of GDM in a previous pregnancy is the strongest risk factor for GDM, with reported recurrence rates of up to 84% ( 128 ). The risk of recurrence varies greatly depending on ethnicity ( 128 ). Ethnicities at increased risk for development of type 2 diabetes, such as South and East Asians, Hispanic, Black and Native Americans, Aboriginal and Torres Strait Islanders, and Middle Easterners are also associated with an increased risk of GDM ( 129 , 130 ). A US study of over 123 000 women reported the prevalence of GDM using the 2000 ADA diagnostic criteria to be the highest among Filipinas (10.9%) and Asians (10.2%), followed by Hispanics (6.8%), non-Hispanic Whites (4.5%) and Black Americans (4.4%) ( 131 ). Women who have had GDM are at increased risk for subsequent type 2 diabetes, while family history of type 2 diabetes in a first-degree relative or sibling with GDM is a major risk factor for GDM ( 129 , 132-134 ).

Key risk factors for gestational diabetes mellitus

Abbreviations: BMI, body mass index; GDM, gestational diabetes mellitus.

Increasing maternal age is also a risk factor for GDM ( 129 , 133-135 ). The prospective First and Second Trimester Evaluation of Risk trial (n = 36 056) demonstrated a continuous positive relationship between increasing maternal age and risk for adverse pregnancy outcomes, including GDM ( 135 ). Maternal age 35 to 39 years and ≥40 years was associated with an adjusted odds ratio (OR) for GDM of 1.8 (95% CI 1.5-2.1) and 2.4 (95% CI 1.9-3.1), respectively ( 135 ). Other studies in high-risk cohorts have reported a lesser risk between increasing maternal age and GDM after adjustment for other risk factors ( 136 ).

Maternal prepregnancy overweight (BMI 25-29.99 kg/m 2 ) or obesity (BMI ≥ 30 kg/m 2 ) are common risk factors for GDM ( 129 , 130 , 133 , 134 , 136 , 137 ). The risk of GDM is increased almost 3-fold (95% CI 2.1-3.4) in women with class I obesity (BMI 30-34.99 kg/m 2 ) and 4-fold (95% CI 3.1-5.2) in women with class II obesity (BMI 35-39.99 kg/m 2 ), compared to women with a BMI < 30 kg/m 2 ( 138 ). High GWG, particularly in the first trimester, is also associated with an increased risk for GDM ( 131 , 139 , 140 ). Further, women with obesity and high GWG are 3- to 4-fold more likely to develop abnormal glucose tolerance compared to women who remained within the 1990 Institute of Medicine (IOM) recommendations for GWG ( 131 , 141 ). Interpregnancy weight gain is also a risk factor for GDM and perinatal complications in a subsequent pregnancy ( 142 ) and may be a potential confounder when considering the risk of GDM recurrence.

Studies have demonstrated an association between polycystic ovary syndrome and GDM, although this is significantly attenuated after adjustment for maternal BMI ( 143 , 144 ). Other risk factors for GDM include multiparity ( 133 , 134 ), twin pregnancy ( 145 , 146 ), previous macrosomia ( 123 ), a history of perinatal complications ( 134 ), maternal small-for-gestational-age (SGA) or LGA ( 134 ), physical inactivity ( 129 , 147 , 148 ), low-fiber high-glycemic load diets ( 149 ), greater dietary fat and lower carbohydrate intake ( 137 ), and medications such as glucocorticoids and anti-psychotic agents ( 150 , 151 ). Maternal pre- and early pregnancy hypertension is also associated with an increased risk of developing GDM ( 152 , 153 ).

Overall, noting the variation in performance and utility of clinical risk factors based on local population factors, previous GDM and family history of diabetes appear to be the strongest clinical risk factors for GDM ( 154-157 ). Ethnicity, higher maternal age, and BMI are also strong predictors for GDM ( 154-158 ).

Normal pregnancy is associated with marked changes in glycemic physiology ( 159 , 160 ). There is a progressive increase in insulin resistance, predominantly due to increased circulating placental hormones including growth hormone, corticotrophin-releasing hormone, human placental lactogen, prolactin, estrogen, and progesterone ( 161-166 ). Increased maternal adiposity particularly in early pregnancy also promotes insulin resistance, contributing to facilitated lipolysis by late pregnancy ( 167 , 168 ). The resultant increase in maternal free fatty acid (FFA) levels exacerbates maternal insulin resistance by inhibiting maternal glucose uptake and stimulating hepatic gluconeogenesis ( 168 , 169 ). By late pregnancy, studies have reported decreases in maternal glucose sensitivity between 40% and 80% in women with normal or increased BMI ( 170-172 ). Increased maternal insulin resistance results in higher maternal postprandial glucose levels and FFAs for maternal growth ( 164 , 167 , 173 ) and increased facilitated diffusion across the placenta, leading to greater availability of glucose for fetal growth ( 161 , 174 ). This progressive rise in maternal insulin resistance underpins the delayed testing approach to GDM, aiming to maximize detection of GDM when insulin resistance is at its greatest in mid- to late gestation.

In addition to increased insulin resistance and elevated postprandial glucose, adaptations in normal pregnancy include enhanced insulin secretion ( 160 , 165 ). Maternal glucose levels are maintained at lower levels than in healthy nonpregnant women ( 175 , 176 ), and euglycemia is maintained by a corresponding 200% to 250% increase in insulin secretion, most notable in early pregnancy ( 161 , 167 , 177 ). Human placental lactogen, in addition to prolactin and growth hormone, primarily regulate increased maternal β-cell insulin secretion and proliferation during pregnancy ( 178-180 ). Rodent studies have demonstrated a 3- to 4-fold increase in β-cell mass during pregnancy, mediated via hypertrophy, hyperplasia, neogenesis, and/or reduced apoptosis ( 181 , 182 ).

GDM is characterized by a relative insulin secretory deficit ( 177 ), in which maternal β-cell insulin secretion is unable to compensate for the progressive rise in insulin resistance during pregnancy ( 183 ). This leads to decreased glucose uptake, increased hepatic gluconeogenesis, and maternal hyperglycemia ( 167 ). It is hypothesized that this results from the failure of β-cell mass expansion ( 182 , 184 ). Hyperlipidemia, characterized predominantly by higher serum triglycerides, may also cause lipotoxic β-cell injury, further impairing insulin secretion ( 185 , 186 ). The pathogenesis of GDM therefore parallels that of type 2 diabetes, characterized by both increased insulin resistance and relative insulin deficiency arising from a reduction in β-cell function and mass ( 187 , 188 ).

Serial studies of the insulin secretory response in women who develop GDM suggest that the abnormal insulin secretory response is present from prepregnancy and increases in early pregnancy, prior to and independent of changes in insulin sensitivity ( 170 , 189-191 ). These data suggest that many women with GDM may have chronic or preexisting β-cell dysfunction, potentially mediated by circulating hormones including leptin ( 191 ).

The genetics of GDM and glucose metabolism in pregnancy remain poorly defined. Data on epigenetic mechanisms in GDM are especially lacking and primarily limited to the potential role of DNA methylation in mediating the intrauterine effects of GDM on offspring outcomes ( 192 , 193 ).

Most genetic studies have focused on variants associated with type 2 diabetes and have demonstrated a similar association with GDM ( 194 , 195 ). A meta-analysis of 28 case-control studies (n = 23425) ( 196 ) identified 6 genetic polymorphisms at loci involved in insulin secretion [insulin-like growth factor 2 messenger RNA-binding protein 2 ( IGF2BP2 ), melatonin receptor 1B ( MTNR1B ) and transcription factor 7-like 2 ( TCF7L2 )] ( 197-199 ), insulin resistance [insulin receptor substrate 1 ( IRS1 ) and peroxisome proliferator-activated receptor gamma ( PPARG )] ( 200 , 201 ), and inflammation [tumor necrosis factor alpha ( TNF-α )] ( 202 ) in type 2 diabetes. Overall, only MTNR1B , TCF7L2 , and IRS1 were also significantly associated with GDM, supporting the role of both impaired insulin secretion and insulin resistance in the pathogenesis of GDM as well as type 2 diabetes ( 196 ). Subgroup analysis showed the risk alleles of TCF7L2 and PPARG were significant only in Asian populations, while the association between IRS1 and TCF7L2 and GDM risk varied depending on diagnostic criteria and genotype methodology ( 196 ), highlighting the need for further large confirmatory studies.

Two genome-wide association studies (GWAS) have evaluated the genetic associations for GDM and glucose metabolism ( 194 , 203 ). The first, a 2-stage GWAS in Korean women, compared 468 women with GDM and 1242 normoglycemic women using 2.19 million genotyped markers before further genotyping 11 loci in 1714 women, identifying 2 loci significantly associated with GDM ( 203 ). A variant in cyclin-dependent kinase 5 regulatory subunit-associated protein 1-like 1 ( CDKAL1 ) had the strongest association with GDM, followed by a variant near MTNR1B expressed in pancreatic β-cells ( 204 ). The IGF2BP2 variant did not reach genome-wide significance with GDM in this study. CDKAL1 was significantly associated with decreased fasting insulin concentration and homeostasis model assessment of β-cell function in women with GDM, consistent with impaired β-cell compensation. MTNR1B was associated with decreased fasting insulin concentrations in women with GDM and increased fasting glucose concentrations in both women with and without GDM ( 203 ). Variants in CDKAL1 and MTNR1B have previously been associated with type 2 diabetes risk ( 205 , 206 ).

A subsequent GWAS performed in a subset of the HAPO cohort (n = 4528) comprising European, Thai, Afro-Caribbean, and Hispanic women evaluated maternal metabolic traits in pregnancy ( 194 ). This study reported 5 variants associated with quantitative glycemic traits in the general population ( 207 , 208 ) that were also associated with glucose or C-peptide levels in pregnancy, although strength of association varied across cohorts ( 194 ). Specifically, loci in glucokinase regulator ( GCKR ), glucose-6-phosphatase 2 ( G6PC2 ), proprotein convertase subtilisin/kexin type 1 ( PCSK1 ), protein phosphatase 1, regulatory subunit 3B ( PPP1R3B ), and MTNR1B were associated with fasting glucose. In addition, GCKR and PPP1R3B were associated with fasting C-peptide levels, while MTNR1B was associated with 1-hour postload glucose. These loci have also previously been associated with lipid metabolism ( GCKR and PPP1R3B ), glycogen metabolism ( PPP1R3B ), and obesity-related traits ( PCSK1 ) ( 209-214 ).

Two additional novel loci identified near hexokinase domain containing 1 ( HKDC1 ) associated with 2-hour postload glucose, and β-site amyloid polypeptide cleaving enzyme 2 ( BACE2 ) associated with fasting C-peptide, demonstrated limited association with glycemic traits outside of compared to in pregnancy ( 215 ). In general, however, studies evaluating associations between genetic risk scores, glycemic traits in pregnancy, and GDM have also confirmed that genetic determinants of fasting glucose and insulin, insulin secretion, and insulin sensitivity reported outside of pregnancy influence GDM risk ( 216 ). A summary of the genes associated with GDM is provided in Table 5 .

Genes linked to gestational diabetes mellitus

Genes were identified and selected from the genome-wide association studies ( 194 , 203 ). The name and function of each gene was determined from GeneCards ( https://www.genecards.org ).

a Collectrin, amino acid transport regulator is a stimulator of β-cell replication.

Maturity-onset diabetes of the young (MODY) is the most common form of monogenic diabetes; inherited forms of diabetes characterized by defects in single genes regulating β-cell development and function ( 217 , 218 ). MODY consists of several autosomal dominant forms of diabetes accounting for up to 2% of all diabetes diagnoses ( 219 ). A diagnosis of MODY requires confirmatory molecular genetic testing, and thus MODY is frequently misdiagnosed as preexisting diabetes or GDM, accounting for up to 5% of GDM “cases” ( 220-223 ). A UK study reported that HNF-1α (MODY3) (52%) and glucokinase (GCK)-MODY subtype (MODY2) (32%) were most frequent in probands confirmed with MODY, followed by HNF-4α (MODY1) and HNF-1β (MODY5) ( 224 ).

Women with GCK-MODY often first present following antenatal screening for GDM, with an estimated prevalence of 1% of all GDM “cases” actually GCK-MODY ( 220 , 222 ). GCK-MODY is caused by mutations in the glucokinase gene, leading to a greater set point for glucose stimulated insulin release ( 219 ). Clinically, GCK-MODY is defined by mild, stable fasting hyperglycemia [fasting glucose 98-150 mg/dL (5.4-8.3 mmol/L)] and low rates of microvascular and macrovascular complications ( 220 ). It should be suspected following a positive OGTT in pregnancy if the fasting glucose is ≥5.5 mmol/L, the glucose increment from the fasting to 2-hour (75-g) OGTT is small (<4.6 mmol/L), and there is a positive family history of mild hyperglycemia or diabetes. In addition, a combination of fasting glucose ≥ 100 mg/dL (5.6 mmol/L) and BMI < 25 kg/m 2 has been shown to have a sensitivity of 68% and a specificity of 99% for differentiating GCK-MODY from GDM ( 220 ). Importantly, management differs from that of GDM because the need for intensive maternal glycemic control largely depends on whether the GCK-MODY mutation is also present in the fetus ( 220 , 225 , 226 ). Maternal insulin therapy is therefore only recommended in the presence of increased fetal abdominal growth (>75th centile) measured on serial ultrasounds from 26 weeks’ gestation, as this indicates that the fetus does not have the GCK mutation ( 220 ).

GDM is associated with excess neonatal and maternal short- and long-term morbidity, summarized in Table 6 .

Maternal and neonatal complications of gestational diabetes mellitus

Sources: Scholtens et al ( 227 ) and Saravanan ( 228 ).

Abbreviation: GDM, gestational diabetes mellitus.

The Pedersen hypothesis describes the pathophysiology contributing to perinatal complications in GDM ( 229 ). Maternal hyperglycemia results in fetal hyperglycemia via facilitated diffusion of glucose by the glucose transporter 1 (GLUT1) ( 230 ). Fetal hyperglycemia results in fetal hyperinsulinemia, promoting fetal anabolism, excessive fetal adiposity, and accelerated growth, leading to LGA and macrosomia ( 231-239 ). Maternal hyperlipidemia also contributes to excess fetal growth ( 233 , 240 ). Macrosomia and LGA increase the risk of cesarean section, birth trauma, and perinatal complications including shoulder dystocia, brachial plexus injury and fracture, and perinatal asphyxia ( 27 , 132 , 237 , 238 , 241-243 ). Increased risk of perinatal asphyxia is associated with fetal death in utero, polycythemia, and hyperbilirubinemia ( 27 , 244-246 ). Fetal hyperinsulinemia can also increase the risk of metabolic abnormalities including neonatal hypoglycemia, hyperbilirubinemia, and respiratory distress syndrome postpartum ( 27 , 244 ). The risk appears to be greater among offspring of women with more severe hyperglycemia ( 247 ). Figure 2 summarizes the perinatal consequences of GDM.

Perinatal consequences of gestational diabetes mellitus.

In the HAPO study, higher maternal glucose levels were associated with an increased risk of LGA, shoulder dystocia or birth injury, and neonatal hypoglycemia ( 27 ). A recent systematic review (n = 207 172) confirmed similar positive linear associations for maternal glycemia based on maternal glucose thresholds for the GCT, 75-g 2-hour OGTT, or 100-g 3-hour OGTT and risk of cesarean section, induction of labor (IOL), LGA, macrosomia, and shoulder dystocia ( 248 ). GDM has also been associated with an increased risk of preterm birth, birth trauma, neonatal respiratory distress syndrome, and hypertrophic cardiomyopathy ( 27 , 244 , 249 ). An increased risk of congenital malformations in the offspring has been reported, although whether this persists after adjustment for maternal age, BMI, ethnicity, and other contributing factors is unknown ( 250 ). A French cohort study (n = 796 346) reported a 30% higher risk of cardiac malformations in the offspring of women with GDM compared to women with normal glucose tolerance, after excluding women with likely undiagnosed pregestational diabetes ( 249 ). However, this increased risk only reached statistical significance in women treated with insulin therapy. Maternal BMI, which was not evaluated in these studies, may account for these findings ( 251 , 252 ). Similarly, a reported increase in perinatal mortality after 35 weeks’ gestation in the offspring of women with GDM may also be confounded by obesity ( 253-256 ). An increased risk of perinatal mortality after 37 weeks’ gestation was demonstrated in French women with GDM on dietary intervention, possibly because these women delivered later than women treated with insulin therapy ( 249 ). In contrast, the HAPO study did not demonstrate excess perinatal mortality in their untreated cohort ( 27 ).

Modern management of GDM and associated maternal risk factors is associated with near-normal birthweight in developed countries ( 115 , 257 ). This is important because birthweight is the major risk factor for shoulder dystocia, brachial plexus injury, neonatal hypoglycemia, and neonatal respiratory distress syndrome in the offspring of women with and without GDM ( 242 ). A retrospective cohort study of 36 241 pregnancies in the United States reported that the risk of shoulder dystocia among infants of women without GDM compared to women with GDM was 0.9% vs 1.6% if birthweight was <4000 g and 6.0% vs 10.5% if birthweight was ≥4000 g (macrosomia) ( 242 ). The risk of neonatal hypoglycemia in infants with birthweight < 4000 g was 1.2% vs 2.6% and 2.4% vs 5.3% for birthweight ≥ 4000 g, in women without GDM compared to women with GDM, respectively. Similar findings were seen for brachial plexus injury and neonatal respiratory distress syndrome. Thus, GDM confers increased risk of perinatal complications independent of birthweight.

The risk of stillbirth is also greater in women with GDM. A large US retrospective analysis examined stillbirth rates at various stages of gestation in over 4 million women, including 193 028 women with GDM. The overall risk of stillbirth from 36 to 42 weeks’ gestation was higher in women with GDM compared to women without GDM (17.1 vs 12.7 per 10 000 deliveries; RR 1.34; 95% CI 1.2-1.5) ( 253 ). This increased risk of stillbirth was also observed at each gestational week: 3.3 to 8.6 per 10 000 ongoing pregnancies in women with GDM compared to 2.1 to 6.4 per 10 000 ongoing pregnancies in women without GDM from 36 to 41 weeks’ gestation ( 253 ). For women with GDM, the relative risk of stillbirth was highest in week 37 (RR 1.84, 95% CI 1.5-2.3). Notably, the risk of stillbirth is highest in women with undiagnosed GDM. In a UK prospective case-control study (n = 1024), women with undiagnosed GDM based on a fasting glucose level ≥ 5.6mmol/L (≥100 mg/dL) had a 4-fold greater risk of late stillbirth (defined as occurring ≥28 weeks’ gestation) compared to women with fasting glucose < 5.6mmol/L (<100 mg/dL) ( 74 ). In contrast, women at risk of GDM based on NICE risk factors who were diagnosed with GDM on the OGTT had a similar risk of stillbirth to women who were not at risk of GDM. This suggests that diagnosing and managing GDM reduces the risk of stillbirth to near-normal levels ( 74 ).

Recent epidemiological studies suggest an increased risk of later adverse cardiometabolic sequelae in the offspring of women with GDM ( 227 , 258 ). A large Danish population-based cohort study (n = 2 432 000) demonstrated an association between maternal diabetes and an increased rate of early onset cardiovascular disease (CVD; ≤40 years of age) among offspring ( 259 ). GDM specifically was associated with a 19% increased risk of early onset CVD (95% CI 1.07-1.32). A longitudinal UK study provides potential mechanistic insight, finding that GDM was associated with alterations in fetal cardiac function and structure, with reduced systolic and diastolic ventricular function persisting in infancy ( 260 ). This is consistent with the association between in utero exposure to maternal hyperglycemia and fetal programming first reported in the Native American Pima population, characterized by a high prevalence of obesity, type 2 diabetes, and GDM ( 261 ).

The recent HAPO Follow Up Study (HAPO-FUS), which was not confounded by treatment of maternal glycemia, included 4832 children 10 to 14 years of age whose mothers were participants of HAPO ( 227 ). The HAPO-FUS demonstrated a durable impact of maternal glycemia with long-term offspring glucose metabolism, including at glucose levels lower than those diagnostic for GDM ( 227 ). A generally linear relationship between maternal antenatal glucose and offspring glucose levels and related outcomes was observed. Increasing maternal glucose categories were associated with a higher risk of impaired fasting glucose and impaired glucose tolerance and higher timed glucose measures and HbA1c levels and were inversely associated with insulin sensitivity and disposition index by 14 years of age, independent of maternal and childhood BMI and family history of diabetes ( 227 ). A positive association was observed between GDM defined by any criteria and glucose levels and impaired glucose tolerance in the offspring at ages 10 to 14 years and an inverse association with offspring insulin sensitivity ( 262 ). Higher frequencies of childhood obesity and measures of adiposity across increasing categories of maternal OGTT glucose levels were also noted ( 262 ). Recent evidence for increased glucose-linked hypothalamic activation in offspring aged 7 to 11 years previously exposed to maternal obesity and GDM in utero, which predicted higher subsequent BMI, represents 1 possible mechanism for this increased childhood obesity risk ( 263 ).

Women with GDM are at an increased risk of obstetric intervention including IOL, cesarean section ( 27-29 , 264 , 265 ), and complications associated with delivery including perineal lacerations and uterine rupture, predominantly relating to fetal macrosomia and polyhydramnios ( 266 ).

As demonstrated in HAPO and other studies, women with GDM also have an increased risk of gestational hypertension and preeclampsia ( 267-269 ). Consistent with the association between diabetes and microvascular disease, abnormalities in glucose metabolism affect trophoblast invasion, leading to impaired placentation and greater risk for preeclampsia ( 270 ). The mechanism likely relates to insulin resistance and inflammatory pathway activation ( 271 , 272 ), with in vitro studies showing that elevated glucose concentrations inhibit trophoblast invasiveness by preventing uterine plasminogen activator activity ( 272 ).

Long-term Maternal Risk Following GDM

Women diagnosed with GDM based on pre-IADPSG diagnostic criteria are at increased risk of GDM in future pregnancies, with reported recurrence rates of 30% to 84% ( 128 ). A diagnosis of GDM is also associated with up to a 20-fold greater lifetime risk of type 2 diabetes ( 273 , 274 ). A recent large meta-analysis and systematic review (20 studies, n = 1 332 373 including 67 956 women with GDM) showed that women with a history of GDM have a 10-fold increased risk of developing type 2 diabetes, mostly within the first 5 years post-GDM ( 273 ). HAPO-FUS demonstrated that over 50% of women whose OGTT thresholds met (untreated) IADPSG diagnostic criteria for GDM had developed impaired glucose tolerance after 14 years of follow-up ( 275 ). These data highlight the importance of a management approach to GDM that focuses on early prevention of type 2 diabetes. For example, the updated NICE guidelines now recommend diabetes prevention for all women with previous GDM ( 276 , 277 ).

Previous GDM is also associated with cardiovascular risk factors such as obesity, hypertension, and dyslipidemia ( 274 , 278-280 ). The lifetime risk of cardiovascular disease following GDM is almost 3-fold higher in women who develop type 2 diabetes and 1.5 fold higher even in women without type 2 diabetes ( 280 ). Studies also report a 26% greater risk of hypertension and a 43% greater risk of myocardial infarction or stroke in women with previous GDM compared to women without GDM ( 281 , 282 ). The significance of GDM as a risk factor for type 2 diabetes and cardiovascular disease has been recently recognized by international organizations including the American Heart Association ( 283 ).

Benefits of Intervention on Perinatal Outcomes

Contemporary changes to the detection and management of GDM have been associated with almost comparable neonatal birthweight and adiposity outcomes to the background maternity population in developed countries ( 115 ).

The ACHOIS trial (n = 1000) was the first large RCT to evaluate whether treatment of women with GDM reduced the risk of perinatal complications ( 28 ). GDM was diagnosed based on a combination of fasting glucose < 7.8 mmol/L (140 mg/dL) and 2-hour postload glucose 7.8 to 11.0 mmol/L (140-199 mg/dL), respectively, using the 75-g 2-hour OGTT between 24 and 34 weeks’ gestation, following screening with either positive clinical risk factors or the GCT ( 28 ). ACHOIS demonstrated that a combination of dietary advice, self-monitoring of maternal glucose levels (SMBG), and insulin therapy, if required, to achieve SMBG targets [fasting glucose 3.5-5.5 mmol/L (63-99 mg/dL), preprandial glucose ≤ 5.5 mmol/L (99 mg/dL), and 2-hour postprandial glucose ≤ 7.0 mmol/L (126 mg/dL)], reduced the rate of serious perinatal complications (a composite of death, shoulder dystocia, nerve palsy, and fracture) compared to routine care (1% vs 4%; P  = 0.01). In addition, such interventions were associated with a reduced incidence of macrosomia (10% vs 21%; P  < 0.001), preeclampsia (12% vs 18%; P  = 0.02), and improved maternal health-related quality of life ( 28 ).

In 2009, the MFMU trial (n = 958) reported that treatment of “mild” GDM was also associated with improved outcomes ( 29 ). Following a positive GCT between 24 and 30 + 6 weeks’ gestation, “mild” GDM was defined on a positive 100-g 3-hour OGTT by a fasting glucose < 5.3 mmol/L (95 mg/dL), and at least 2 postload glucose thresholds that exceeded the 2000 ADA diagnostic thresholds [1-, 2-, or 3-hour thresholds 10.0 mmol/L (180 mg/dL), 8.6 mmol/L (155 mg/dL), and 7.8 mmol/L (140 mg/dL), respectively]. Women with previous GDM were excluded from the study. Dietary intervention, SMBG, and insulin therapy, if required, to achieve a fasting glucose target < 5.3 mmol/L (95 mg/dL) and 2-hour postprandial glucose target < 6.7 mmol/L (121 mg/dL) was associated with reduced rates of macrosomia (5.9% vs 14.3%; P  < 0.001), LGA (7.1% vs 14.5%; P  < 0.001), shoulder dystocia (1.5% vs 4.0%; P  = 0.02), cesarean section (26.9% vs 33.8%; P  = 0.02), and preeclampsia and gestational hypertension (8.6% vs 13.6%; P  = 0.01) compared to routine care. However, the intervention did not lead to a significant difference in the primary composite outcome of stillbirth, perinatal death, and neonatal complications (hyperbilirubinemia, hypoglycemia, hyperinsulinemia, and birth trauma) ( 29 ). Treatment targets in the MFMU trial were lower than that of the ACHOIS trial, and whether this may account for the reduction in cesarean section not shown in the ACHOIS trial is unclear. These key findings, supported by other studies ( 22 , 284 ), were highlighted by the IADPSG to support the lowering of the GDM diagnostic criteria and treating mild hyperglycemia ( 30 ).

A recent Cochrane review (8 RCTs; n = 1418) reported that GDM treatment, including dietary intervention and insulin therapy, reduced a composite outcome of perinatal morbidity (death, shoulder dystocia, bone fracture, and nerve palsy) by 68% compared to routine antenatal care ( 285 ). Treatment was also associated with reductions in macrosomia, LGA, and preeclampsia but an increase in IOL and neonatal intensive care admission.

The main objective of GDM management is to attain maternal normoglycemia because evidence suggests that excessive fetal growth can be attenuated by maintaining near normal glucose levels ( 286 , 287 ). The foundation of this approach is medical nutrition therapy. Given carbohydrates are the primary determinant of maternal postprandial glucose levels, current dietary practice aims to modify carbohydrate quality (glycemic index) and distribution ( 32 , 288 , 289 ). The original nutritional approach for GDM decreased total carbohydrate intake to 33% to 40% of total energy intake (EI) and was associated with reduced postprandial glycemia and fetal overgrowth ( 290 ). More recent evidence suggests that higher carbohydrate intake and quality (lower glycemic index) between 60% and 70% EI can also limit maternal hyperglycemia ( 291-293 ). Nevertheless, there remain limited data to support a specific dietary intervention for GDM ( 294 ). A recent meta-analysis (18 RCTs; n = 1151) showed that enhancing nutritional quality (modified dietary intervention, defined as a dietary intervention different from the usual one used in the control group) after GDM diagnosis, irrespective of the specific dietary approach, improved maternal fasting and postprandial glycemia, and reduced pharmacotherapy requirements, birthweight, and macrosomia ( 295 ).

Guidelines therefore currently recommend a range of carbohydrate intake between 33% and 55% EI ( 32 , 288 , 289 ). Studies have reported improved pregnancy outcomes in GDM with both lower carbohydrate (42%E) and high‐carbohydrate (55%E) diets ( 296 ), reflected in the most recent Academy of Nutrition and Dietetics guidelines, which state that beneficial effects on pregnancy outcomes in GDM are seen with a range of carbohydrate intakes ( 288 ). The IOM guidelines recommend a carbohydrate intake of at least 175 g/day and a total daily caloric intake of 2000 to 2500 kilocalories during pregnancy ( 289 ). The ACOG recommends a lower carbohydrate diet (33-40%E) ( 297 ). However, the ADA has raised concerns over the corresponding higher maternal fat intake, fetal lipid exposure, and overgrowth resulting from lowering carbohydrate intake ( 298 ) and withdrew specific dietary guidelines for GDM in 2005 ( 299 ).

Given maternal glucose primarily supports fetal growth and brain development ( 300 ), theoretically if the maternal diet is too low in carbohydrate, the maternal-fetal glucose gradient may be compromised. Restriction of total maternal EI is associated with reduced fetal growth ( 301 ). A recent systematic review similarly showed that lower carbohydrate intake correlated with lower birthweight and greater incidence of SGA ( 302 ), with a lower carbohydrate threshold of 47% EI associated with appropriate fetal growth ( 302 , 303 ). Importantly, the lower carbohydrate threshold independent of energy restriction in GDM is yet to be established. Related safety concerns with lower carbohydrate diets include the potential risk of higher fetal exposure to maternal ketones ( 304 ) and micronutrient deficiency ( 305 , 306 ). In vitro studies have shown that ketones suppress trophoblast uptake of glucose, jeopardizing glucose transfer across the placenta ( 307 ). Clinically, a prospective US cohort study of women with preexisting diabetes, GDM, or normal glucose tolerance demonstrated an inverse correlation between higher maternal third trimester beta-hydroxybutyrate and FFAs and lower offspring intellectual development scores at 2 to 5 years of age, although total carbohydrate, EI, and maternal BMI were not reported ( 304 ).

The IOM has published recommendations for weight gain during pregnancy based on prepregnancy BMI ( 289 ), but no specific recommendations for weight gain in GDM exist ( 286 ). In women with overweight or obesity, studies have suggested that weight reduction or gain ≤ 5 kg increased the risk of SGA ( 308 ). A recent systematic review based on data from almost 740 000 women demonstrated that GWG of 5 kg to 9 kg in women with class I obesity (BMI 30-34.99 kg/m 2 ), 1 to <5 kg for class II obesity (35-39.99 kg/m 2 ), and no GWG for women with class III obesity (BMI ≥ 40kg/m 2 ), minimized the combined risk of LGA, SGA, and cesarean section ( 309 ).

A meta-analysis (n = 88 599) evaluating the relationship between GWG and pregnancy outcomes in GDM specifically showed that GWG greater than the IOM recommendations was associated with an increased risk of pharmacotherapy, as well as of hypertensive disorders of pregnancy, cesarean section, LGA, and macrosomia ( 310 ). GWG below the IOM recommendations was protective for LGA (RR 0.71; 95% CI 0.56-0.90) and macrosomia (RR 0.57; 95% CI 0.40-0.83) and did not increase the risk of SGA (RR 1.40; 95% CI 0.86-2.27) ( 289 ). This suggests that GWG targets in GDM may need to be lower than the current recommendations for normal pregnancy. However, from a practical perspective, only 30% of women gained less than the recommended IOM GWG targets ( 310 ).

Fasting and postprandial glucose testing with either the 1- or 2-hour postprandial glucose value is recommended in women with GDM. The 1-hour postprandial glucose approximates to the peak glucose excursion in pregnancy in women without diabetes and those with type 1 diabetes ( 175 ). Studies have shown that the 1-hour postprandial peak glucose level correlates with amniotic fluid insulin levels, reflecting fetal hyperinsulism ( 311 ) and with fetal abdominal circumference in women with type 1 diabetes ( 286 ). An RCT that compared pre- to postprandial maternal SMBG values showed that titrating insulin therapy based on the 1-hour postprandial values was associated with improved maternal glycemic control and may better attenuate the risk of neonatal complications attributed to fetal hyperinsulinemia ( 312 ).

Treatment targets based on maternal SMBG levels vary internationally ( Table 7 ). There is some suggestion that lower glucose targets may improve pregnancy outcomes in GDM ( 176 , 313 , 314 ), but this is yet to be evaluated in adequately powered RCTs. Conversely, lower glycemic targets may be associated with an increased risk of SGA ( 315-317 ) and maternal and fetal hypoglycemia ( 318 , 319 ). A small study evaluating stringent glycemic targets in 180 women with GDM failed to demonstrate additional benefits, with no differences in the rates of cesarean section, birthweight, macrosomia, or SGA in the offspring of women randomized to intensive [preprandial glucose ≤ 5.0 mmol/L (90 mg/dL) and 1-hour postprandial glucose ≤ 6.7 mmol/L (121 mg/dL)] compared to standard treatment targets [preprandial glucose ≤ 5.8 mmol/L (104.5 mg/dL) and 1-hour postprandial glucose ≤ 7.8 mmol/L (140 mg/dL)] ( 320 ).

Recommended glycemic treatment targets in GDM

Abbreviations: ACHOIS, Australian Carbohydrate Intolerance Study in Pregnant Women Study; ADA, American Diabetes Association; ADIPS, Australasian Diabetes in Pregnancy Society; CDA, Canadian Diabetes Association; NICE, UK National Institute for Health and Care Excellence; MFMU, National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network.

Insulin has traditionally been the preferred treatment for GDM if maternal glucose levels remain elevated on medical nutrition therapy ( 267 ). Depending on targets, approximately 50% of women with GDM are prescribed insulin therapy to maintain normoglycemia ( 321 , 322 ), with a combination of evening intermediate-acting insulin if fasting glucose levels are elevated and mealtime rapid-acting insulin when indicated. Additional daytime intermediate-acting insulin may also be needed to control prelunch or predinner hyperglycemia.

Decreasing insulin doses in the third trimester may simply reflect the physiological increase in maternal insulin sensitivity observed at this stage of pregnancy ( 176 , 323 ). However, substantial insulin dose reduction, recurrent maternal hypoglycemia, and/or slowing of fetal growth or preeclampsia may indicate underlying pathophysiological placental insufficiency ( 324 ), impacting the timing of delivery and intensity of obstetric monitoring.

Risk factors for insulin therapy include earlier diagnosis of GDM ( 81 ), the pattern and degree of elevation of the 75-g 2-hour OGTT diagnostic glucose thresholds ( 325 ), and ethnicity ( 325 ). Other risk factors including gestational age and HbA1c level at the time of GDM diagnosis, BMI, and family history of diabetes account for only 9% of the attributable risk for insulin therapy ( 321 ). A recent Australian study found that maternal age > 30 years, family history of diabetes, prepregnancy obesity, previous GDM, early diagnosis of GDM, fasting glucose ≥ 5.3 mmol/L (96 mg/dL) and HbA1c ≥ 5.5% (37 mmol/mol) at diagnosis were all independent predictors for insulin therapy ( 326 ). Insulin usage could also be estimated according to the number of predictors present, with up to 93% of women with 6 to 7 predictors using insulin therapy compared with less than 15% of women with 0 to 1 predictors ( 326 ).

Oral pharmacotherapy options include glyburide and metformin. Oral pharmacotherapy is associated with improved cost effectiveness, compliance, and acceptability compared to insulin therapy ( 327 ). However, there are issues regarding efficacy and safety, particularly longer term, and thus insulin is generally preferred as first-line pharmacotherapy following lifestyle intervention.

Glyburide is commonly prescribed as first-line therapy for GDM in the United States ( 328 ). An early study evaluating the efficacy of glyburide vs insulin therapy in 404 women with GDM reported no differences in maternal glucose levels or neonatal outcomes between the treatment groups ( 329 ). However, subsequent studies show that approximately 20% of women treated with glyburide required additional insulin therapy to achieve adequate maternal glycemia ( 330 ). Moreover, a large retrospective US study of almost 111 000 women with GDM, in which 4982 women were treated with glyburide and 4191 women were treated with insulin, reported that glyburide was associated with an increased risk of neonatal complications including neonatal intensive care admission, respiratory distress syndrome, hypoglycemia, birth injury, and LGA compared to insulin therapy ( 331 ). Although transplacental transfer of glyburide to the fetus is highly variable, it can reach 50% to 70% of maternal plasma concentration ( 332 ), potentially causing direct stimulation of fetal insulin production ( 333 ).

The use of metformin in pregnancy continues to rise ( 334 ). However, its use remains controversial, due to the potential concerns regarding long-term metabolic programming effects of placental transfer of metformin to the fetus, with some studies suggesting similar plasma concentrations of metformin in the maternal and fetal circulation ( 335 ). A recent systematic review and meta-analysis of 28 studies (n = 3976) evaluating growth in offspring of women with GDM exposed to metformin compared to insulin therapy found that neonates exposed to metformin had lower birthweights (mean difference −107.7 g; 95% CI −182.3 to −32.7), decreased risk of LGA (OR 0.78; 95% CI 0.62-0.99), and macrosomia (OR 0.59; 95% CI 0.46-0.77) and lower ponderal indices than neonates whose mothers were treated with insulin ( 336 ). No difference in the risk of SGA was found, in contrast to outcomes in women with type 2 diabetes, with the Metformin in Women with Type 2 Diabetes RCT observing more than double the rate of SGA (95% CI 1.16-3.71) in the metformin treated cohort, in association with lower insulin doses, HbA1c, and GWG ( 337 ). Offspring of women with GDM exposed to metformin also demonstrate accelerated postnatal growth at 18 to 24 months of age (2 studies; n = 411; mean difference in weight 440 g; 95% CI 50-830), resulting in higher BMI at 5 to 9 years of age (3 studies; n = 520; BMI mean difference 0.78 kg/m 2 , 95% CI 0.23-1.33) ( 336 ).

The Metformin in Gestational Diabetes trial randomized 751 women to receive either metformin or insulin therapy, finding no significant difference in the composite neonatal outcome of neonatal hypoglycemia, respiratory distress syndrome, hyperbilirubinemia, low Apgar scores, birth trauma, and preterm birth ( 322 ). There was a trend toward increased preterm birth and decreased maternal GWG in women treated with metformin, while severe neonatal hypoglycemia was highest in those treated with insulin. Almost 50% of women treated with metformin required the addition of insulin therapy ( 322 ). Other studies have reported that between 14.0% and 55.8% of women treated with metformin also require insulin therapy to achieve optimal glycemic control ( 338 , 339 ). The Metformin in Gestational Diabetes: The Offspring Follow-Up 2-year follow-up study found that children exposed to metformin had increased subcutaneous fat localized to the arm compared with children whose mothers were treated with insulin alone ( 340 ). By 7 and 9 years of age the children exposed to metformin had similar offspring total and abdominal body fat percentage and metabolic biochemistry including fasting glucose, insulin, and lipids but were larger overall based on measures including weight, arm and waist circumference, waist-to-height ratio, and dual-energy X-ray absorptiometry fat mass and lean mass ( 341 ). These findings are consistent with a recent follow-up study of metformin therapy in pregnant women with polycystic ovary syndrome, which showed that children exposed to metformin in utero had higher BMI and rates of overweight and obesity at 4 years of age ( 342 ).

A recent Cochrane review (8 RCTs; n = 1487) evaluating the use of metformin, glyburide, and acarbose in women with GDM found that the benefits and potential harms of these therapies in comparison to each other are unclear ( 343 ). Other meta-analyses comparing glyburide, metformin, and insulin have shown that metformin was associated with lower GWG, gestational hypertension, and postprandial maternal glucose levels compared to either glyburide or insulin ( 344 , 345 ), but metformin was associated with an increased risk of preterm birth compared to insulin ( 345 ). Compared to metformin, glyburide was associated with a higher risk of increased birthweight, LGA, macrosomia, neonatal hypoglycemia, and increased GWG ( 344 ). More recently, a small RCT (n = 104) suggested that glyburide and metformin were comparable in terms of maternal glycemia and perinatal outcomes ( 346 ). Treatment success after second-line (oral) therapy was higher in the (first-line) metformin vs glyburide cohort (87% vs 50%; P  = 0.03), suggesting that metformin may be the preferred first-line therapy. Overall, most women required either a combination of metformin and glyburide to achieve glycemic control and/or replacement of first-line oral therapy due to hypoglycemia and gastrointestinal side effects, suggesting neither agent alone is likely to be successful in most women with GDM. Combined oral pharmacotherapy had an efficacy rate of 89%, with only 11% of women required third-line therapy with insulin ( 346 ). However, the effects of dual oral therapy crossing the placenta on long-term potential fetal programming via their effects on cellular metabolism, hepatic gluconeogenesis, and insulin sensitivity (metformin) ( 347 ) and fetal hyperinsulinemia (glyburide) is unknown ( 348 ).

A recent Cochrane review consisting of only 3 small RCTs (n = 524) reported insufficient (very low certainty) evidence to evaluate the use of fetal biometry in guiding the medical management of GDM ( 349 ). Nevertheless, serial fetal growth ultrasounds, particularly assessing fetal abdominal circumference, are potentially useful in guiding the intensity of maternal glucose targets and insulin therapy ( 350-352 ). Studies have demonstrated that neonates with an estimated fetal weight ≥ 75th percentile on early third trimester ultrasound were 10-fold more likely to be LGA compared to neonates with an estimated fetal weight < 75th percentile ( 353 ). Measured fetal abdominal circumference < 90th percentile on 2 ultrasounds at 3- to 4-week intervals has also been shown to provide high reliability in excluding the risk of LGA ( 351 ). Moreover, a recent retrospective study (n = 275) found that estimated fetal weight or abdominal circumference up to the 30th percentile on third trimester ultrasound was associated with a greater risk of adverse neonatal outcomes, comparable to that observed with abdominal circumference or estimated fetal weight > 95th percentile in women with hyperglycemia in pregnancy (including GDM) ( 354 ). These findings suggest the potential utility of fetal biometry at thresholds other than defining SGA or LGA in identifying higher risk pregnancies in GDM.

The optimal timing of delivery in GDM is complex, guided by maternal glycemic control in addition to maternal and fetal factors, and has not been definitively established. Current guidelines recommend delivery by 40 + 6 weeks’ gestation in low-risk women with GDM managed with diet alone and from 39 + 0 to 39 + 6 weeks’ gestation for women with GDM well controlled with therapy ( 38 , 277 , 355 ). A recent Canadian population-based cohort study examining the week-specific risks of severe pregnancy complications in women with diabetes included 138 917 women with GDM and 2 553 243 women without diabetes over a 10-year period ( 356 ). There was no significant difference in gestational age-specific maternal mortality or morbidity (defined as ≥1 of the following in the immediate perinatal period: obstetric embolism, obstetric shock, postpartum hemorrhage with hysterectomy or other procedures to control bleeding, sepsis, thromboembolism, or uterine rupture) between iatrogenic delivery and expectant management in women with GDM. However, iatrogenic delivery was associated with an increased risk of neonatal mortality and morbidity (birth or fetal asphyxia, grade 3 or 4 intraventricular hemorrhage, neonatal convulsions, other disturbances of cerebral status of newborn, respiratory distress syndrome, birth injury, shoulder dystocia, stillbirth or neonatal death) at 36 to 37 weeks’ gestation (76.7 and 27.8 excess cases per 1000 deliveries, respectively) but a lower risk of neonatal morbidity and mortality at 38 to 40 weeks’ gestation (7.9, 27.3, and 15.9 fewer cases per 1000 deliveries, respectively) compared with expectant management, suggesting that delivery at 38, 39, or 40 weeks’ gestation may provide the best neonatal outcomes in women with GDM ( 356 ).

Up to one third of women with GDM diagnosed by pre-IADPSG criteria will have glucose levels consistent with diabetes or prediabetes on postpartum testing at 6 to 12 weeks ( 357 ). Thus, a repeat OGTT or fasting glucose as early as 6 to 12 weeks’ postpartum is recommended to confirm maternal glucose status ( 41 , 277 ). Only around 25% of women are tested at this time point with compliance with postpartum testing ranging between 23% and 58% ( 357 , 358 ). In women with GDM with overweight or obesity, a reduction in interpregnancy BMI of ≥2.0 kg/m 2 reduces the risk of subsequent GDM by 74% ( 359 ). Longer term, women should perform regular cardiometabolic health assessment and optimization of lifestyle measures to reduce their greater risk of type 2 diabetes and cardiovascular disease ( 282 , 360 , 361 ). Up to 74% of women with obesity and previous GDM develop type 2 diabetes compared with <25% of women who achieve a normal BMI postpartum following GDM ( 362 ). It is unclear how relevant these studies in older women are for current clinical care given recent data that 50% of women develop type 2 diabetes within 5 to 10 years post-GDM diagnosis ( 273 ). The Diabetes Prevention Program demonstrated that lifestyle intervention and metformin therapy improved insulin sensitivity and preserved β-cell function in women with a history of previous GDM ( 363 ). Early type 2 diabetes prevention following GDM is therefore an essential component of the contemporary GDM detection and management paradigm ( 276 ).

Importantly, despite a reduction in the risk of macrosomia at birth, the ACHOIS and MFMU follow-up studies did not demonstrate a beneficial impact on childhood obesity and glucose tolerance at 5 to 10 years of age in the offspring of women who received treatment for maternal hyperglycemia ( 364 , 365 ). Other prospective cohort studies similarly suggest that the offspring of women with treated GDM still have a greater risk of obesity, type 2 diabetes, the metabolic syndrome, and cardiovascular disease from early childhood and adolescence ( 258 , 366-380 ). For example, a 2017 Danish National Birth Cohort study (n = 561) reported increased adiposity, an adverse cardiometabolic profile, and earlier onset puberty among adolescent females of women with GDM ( 381 ). A prospective offspring cohort study of women with GDM who achieved good antenatal glycemic control demonstrated that offspring adiposity (adipose tissue quantity measured using magnetic resonance imaging) was similar in the GDM and normal glucose tolerance groups within 2 weeks postpartum but was 16.0% greater (95% CI 6.0-27.1; P  = 0.002) by 2 months of age ( 382 ). The mechanism for this greater adiposity and rapid weight gain in early infancy is uncertain given both groups were predominantly breastfed. Consistent with the ACHOIS and MFMU follow-up studies ( 364 , 365 ), these data suggest that the current approach to glycemic control in GDM may not mitigate its impact on longer term infant health. Further, this pathway may be potentially mediated by excess infant adiposity, which correlates with childhood adiposity ( 383 ). Table 8 presents practical tips for managing women with GDM.

Practical tips for managing women with GDM

Abbreviations: GDM, gestational diabetes mellitus; OGTT, oral glucose tolerate test.

Precision medicine seeks to improve diagnostics, prognostics, prediction, and therapeutics in diabetes, including GDM, by evaluating and translating various biological axes including metabolomics, genomics, lipidomics, proteomics, technology, clinical risk factors and biomarkers, and mathematical and computer modeling into clinical practice ( 384 ). The Precision Medicine in Diabetes Initiative was launched in 2018 by the ADA, in partnership with the European Association for the Study of Diabetes, with their first consensus report published in 2020 ( 384 ).

In GDM, precision medicine represents the increasing understanding of heterogeneity within its genotype and phenotype ( 170 , 385-388 ) to identify and translate subclassification of GDM into more personalized clinical care ( 388 ). For example, physiologic subtypes of GDM based on the underlying mechanisms leading to maternal hyperglycemia have been recently characterized ( 386 ). Among 809 women from the Genetics of Glucose Regulation in Gestation and Growth pregnancy cohort, heterogeneity in the contribution of insulin resistance and deficiency to GDM were characterized based on validated indices of insulin sensitivity and secretory response measured during the 75-g OGTT performed between 24 and 30 weeks’ gestation ( 388 ). Compared to women with normal glucose tolerance, women with insulin resistant GDM (51% of GDM) had higher BMI and fasting glucose, hypertriglyceridemia, and hyperinsulinemia, larger infants, and almost double the risk of GDM-associated pregnancy complications. In contrast, women with predominantly insulin secretion defects had comparable BMI, fasting glucose, infant birthweight, and risk of adverse outcomes to those with normal glucose tolerance ( 388 ).

Other studies have also suggested that greater insulin resistance in GDM carries a higher risk of perinatal complications ( 389 ). A recent multicenter prospective study of 1813 women evaluating subtypes of GDM based on insulin resistance ( 389 ) found that women with GDM and high insulin resistance [n = 189 (82.9%)] had a higher BMI, systolic blood pressure, fasting glucose, and lipid levels in early pregnancy compared to women with normal glucose tolerance or those diagnoses with insulin-sensitive GDM. Insulin-sensitive women with GDM [n = 39 (17.1%)] had a significantly lower BMI than women with normal glucose tolerance but similar blood pressure, early pregnancy fasting glucose and lipid levels, and pregnancy outcomes. Despite no differences in insulin treatment and early postpartum glucose intolerance among the GDM subtypes, women with GDM and high insulin resistance had a greater than 2-fold risk of preterm birth and an almost 5-fold increased risk of neonatal hypoglycemia compared with women with normal glucose tolerance. This suggests the high insulin resistance GDM subtype has a greater risk of pregnancy complications potentially arising from the resultant fetal hyperinsulinemia ( 389 ).

The contemporary precision medicine approach to GDM also includes the increasing exploration of early pregnancy risk prediction and risk management models ( 390 ). The traditional binary clinical risk factor approach to identifying women at high risk in early pregnancy is limited by poor sensitivity and specificity, with studies showing that clinical risk factor-based screening fails to identify 10% to over 30% of women with GDM ( 391-396 ). The Pregnancy Outcome for Women with Pre-gestational Diabetes Along the Irish Atlantic Seaboard study found that the prevalence of women with GDM who had no risk factors was low, ranging from 2.7% to 5.4% ( 397 ). However, despite the absence of risk factors, these women with GDM had more pregnancy complications than those with normal glucose tolerance ( 397 ). Other studies have also reported that women without risk factors diagnosed with GDM have comparable pregnancy outcomes to women with GDM identified as high risk ( 393 ). Thus, clinical risk factors alone are not predictive of GDM risk for all women. Although some improvement in the predictive accuracy for GDM is seen in clinical risk scoring approaches ( 158 , 398 ), greater improvement via multivariate risk prediction and mathematical or computer models combining clinical risk factors and biomarkers have been reported in the GDM research setting ( 154-156 , 399-403 ).

Biomarkers are defined as a biological observation that substitutes and ideally predicts the clinically relevant endpoint (ie, GDM) ( 404 ). Biomarker discovery and application in the early detection of GDM has become a major research area. However, few biomarkers are specific enough for clinical application ( 405 ). Most novel biomarkers with potential utility for the prediction of GDM are involved in pathophysiological pathways related to insulin resistance, dyslipidemia, and type 2 diabetes ( 402 , 406 ) but are frequently mediated by maternal obesity ( 240 , 407 ). Early pregnancy risk prediction models for GDM combining clinical risk factors and biomarkers have included various measures of maternal glucose, lipids, adipokines, inflammatory markers, and pragmatic aneuploidy and preeclampsia screening markers, with model performance (area under the curve) up to 0.91 ( 153 , 154 , 399 , 402 , 403 , 408-416 ). Limitations to the clinical application of novel biomarkers and model performance include heterogeneity in the testing approach to GDM and cohort characteristics, potential overestimation of model performance due to overfitting of the data to the index study population, the lack of external clinical validation studies, and limited regulatory guidance for validating biomarker assays ( 405 ).

The COVID-19 pandemic has led to dynamic changes in the testing approach and model of care for women with GDM to minimize the risk of virus transmission and because of decreased clinical capacity. Several temporary pragmatic diagnostic strategies have been suggested as an alternative to the OGTT, including measurement of fasting plasma glucose, random plasma glucose, and HbA1c ( 417-419 ). A secondary analysis of 5974 women from the HAPO study ( 420 ), reported that the UK, Canadian, and Australian COVID-19–modified diagnostic approaches reduced the frequency of GDM by 81%, 82%, and 25%, respectively. Short-term pregnancy complications in the subgroup of women now with undiagnosed GDM (“missed GDM”) were comparable to women diagnosed with GDM based on the Canadian-modified diagnostic criteria, slightly lower for the UK-modified criteria, but significantly lower for the Australasian Diabetes in Pregnancy Association–modified criteria. While all approaches recommend universal testing, the Australian approach adopts a lower fasting glucose threshold of 4.7 mmol/L to identify women who require an OGTT and does not include HbA1c measurement ( 420 ). A retrospective UK study of over 18 000 women sought to define evidence-based recommendations for pragmatic GDM testing during the COVID-19 pandemic ( 421 ), reporting that ~5% of women would be identified as GDM based on a random glucose threshold ≥ 8.5 mmol/L (153 mg/dL) at 12 weeks’ gestation and fasting glucose ≥ 5.2 to 5.4 mmol/L (94-97 mg/dL) or HbA1c ≥ 5.7% (39 mmol/mol) measured at 28 weeks’ gestation. Each test predicted some, but not all, obstetric and perinatal complications, lacking the sensitivity of the OGTT for the diagnosis of GDM but overall may provide adequate risk stratification where the OGTT is not feasible ( 421 ).

GDM is one of the most common complications of pregnancy and is increasing in global prevalence. Diagnosing GDM is important because perinatal complications and stillbirth risk are reduced by treatment. Despite the benefit of identifying and treating GDM, much of the current (short-term) diagnostic and management approach to GDM remains contentious. These differences confound interpretation and application of trial data, preventing a single standard international approach to GDM.

Recent data indicates near normal birthweight and maternity population outcomes in women with GDM based on modern IADPSG criteria in developed countries, demonstrating that even treatment of “milder” maternal hyperglycemia improves pregnancy outcomes. However, most cases of GDM occur in low- and middle-income countries where perinatal risks are far greater and universal 1-step testing may be more practical. There are limited RCT data to guide diagnosis and management in this setting, and further evidence is urgently needed. In developed countries including the United Kingdom, the main issue arguably does not pertain to women diagnosed with GDM but rather high-risk women who remain unscreened (associated with factors such as lower socioeconomic status and higher BMI) who are at highest risk of stillbirth ( 74 ).

The background to the various GDM diagnostic criteria is informative in demonstrating that no approach clearly separates risk groups. It is also now evident that a continuum of risk for GDM exists based on both the timing and degree of maternal hyperglycemia. This underscores the difficulty of defining absolute glucose thresholds at a single timepoint in pregnancy for the diagnosis of GDM and is confounded further by variation in glucose measurement due to preanalytical glucose processing and reproducibility issues. Thus, current diagnostic glucose thresholds for GDM must inevitably reflect compromise and consensus.

A precision medicine approach that recognizes GDM subtype and heterogeneity, enhanced by further research into the genetics of GDM and validation of novel biomarkers and new technologies such as continuous glucose monitoring may improve risk stratification, optimize clinical models of care, and facilitate more individualized and consumer-friendly detection and treatment strategies.

The recent HAPO-FUS data confirming the long-term impact of maternal hyperglycemia on maternal and offspring metabolic health ( 227 , 262 ) highlight an important paradigm shift. The approach to GDM should reflect an evidence base that evaluates diagnostic glucose thresholds and measurement within a framework that includes timing of detection and treatment trials with long-term clinical and health economic outcomes. For example, if the ongoing Treatment of Booking Gestational Diabetes Mellitus trial demonstrates a benefit for early GDM detection and treatment, there are implications for the prevailing diagnostic GDM glucose thresholds in later pregnancy. This is because these thresholds were derived from the risk of perinatal complications in a heterogeneous GDM cohort, which included women who would fulfill early GDM criteria.

Other important areas for research include the evaluation of dietary interventions establishing the optimal carbohydrate threshold in GDM, further clarity on the potential long-term impact of intrauterine metformin on the offspring, as well as the efficacy of preconception and early pregnancy preventive strategies targeting risk factors other than glycemia, such as maternal obesity and GWG. Improved obstetric assessment of placental function, especially in late pregnancy, to inform timing of delivery and identify women at highest risk of stillbirth in GDM is also needed.

The complications of GDM may indeed be greater based on the severity of maternal glycemia and associated vascular risk factors. Nevertheless, the traditional focus on diagnostic criteria and short-term antenatal maternal glucose management fails to address the importance of identifying “milder” (IADPSG-defined) GDM as a risk factor for future maternal and offspring diabetes and CVD risk. It should also be apparent that the increasing prevalence of GDM largely reflects the worsening metabolic health burden including prediabetes and obesity in women of childbearing age. The clinical focus for GDM must therefore urgently shift to early postnatal prevention strategies to decrease the progression from GDM to type 2 diabetes and address longer term maternal and offspring cardiometabolic risk post-GDM via a life course management approach.

A.S. was supported by an NHMRC Fellowship Grant (GNT1148952).

A.S., J.W., H.M., and G.P.R. have nothing to declare.

Bennewitz H. De Diabete Mellito, Gravidatatis Symptomate . MD thesis, University of Berlin ; 1824 .

Google Scholar

Google Preview

Barker DJP. Mothers Babies and Diseases in Later Life. BMJ ; 1994 .

Duncan J . On puerperal diabetes . Trans Obstet Soc Lond. 1882 ; 24 : 256-285 .

Miller HC . The effect of diabetic and prediabetic pregnancies on the fetus and newborn infant . J Pediatr. 1946 ; 29 ( 4 ): 455 - 461 .

Williams J . The clinical significance of glycosuria in pregnant women . Am J Med Sci. 1909 ; 137 : 1 - 26 .

O’Sullivan JB , Mahan CM . Criteria for the oral glucose tolerance test in pregnancy . Diabetes. 1964 ; 13 : 278 - 285 .

Coustan DR . Gestational diabetes. In: Harris MI , Cowie CC , Stern MP , Boyko EJ , Reiber GE , Bennett PH , eds. Diabetes in America . National Institute of Health ; 1995 : 703 - 717 .

World Health Organization . Technical Report Series. No 310. Diabetes Mellitus . Report of a WHO Expert Committee. 1965 .

World Health Organization . Technical Report Series. No 646. Second Report on Diabetes Mellitus . Report of a WHO Expert Committee. 1980 .

World Health Organization . Technical Report Series. No 727. Second Report on Diabetes Mellitus . Report of a WHO Expert Committee. 1985 .

World Health Organisation . Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy: a World Health Organization guideline . Diabetes Res Clin Pract. 2014 ; 103 ( 3 ): 341 - 363 .

O’Sullivan JB , Mahan CM , Charles D , Dandrow RV . Screening criteria for high-risk gestational diabetic patients . Am J Obstet Gynecol. 1973 ; 116 ( 7 ): 895 - 900 .

National Diabetes Data Group . Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance . Diabetes. 1979 ; 28 ( 12 ): 1039 - 1057 .

American College of Obstetricians and Gynecologists . Management of Diabetes Mellitus During Pregnancy . Technical Bulletin No. 92. 1986 .

American Diabetes Association . Gestational diabetes mellitus . Diabetes Care. 1986 ; 9 ( 4 ): 430 - 431 .

Ferrara A , Hedderson MM , Quesenberry CP , Selby JV . Prevalence of gestational diabetes mellitus detected by the National Diabetes Data Group or the Carpenter and Coustan plasma glucose thresholds . Diabetes Care. 2002 ; 25 ( 9 ): 1625 - 1630 .

Carpenter MW , Coustan DR . Criteria for screening tests for gestational diabetes . Am J Obstet Gynecol. 1982 ; 144 ( 7 ): 768 - 773 .

American Diabetes Association . 2. Classification and diagnosis of diabetes: standards of medical care in diabetes-2018 . Diabetes Care. 2018 ; 41 ( suppl 1 ): S13 - S27 .

American College of Obstetricians and Gynecologists . Practice Bulletin No. 190: gestational diabetes mellitus . Obstet Gynecol. 2018 ; 131 ( 2 ): e49 - e64 .

American Diabetes Association . Gestational diabetes mellitus . Diabetes Care. 2000 ; 23 ( suppl 1 ): S77 - S79 .

Sermer M , Naylor CD , Farine D , et al.  The Toronto tri-hospital gestational diabetes project: a preliminary review . Diabetes Care. 1998 ; 21 ( Suppl 2 ): B33 - B42 .

Sermer M , Naylor CD , Gare DJ , et al.  Impact of increasing carbohydrate intolerance on maternal-fetal outcomes in 3637 women without gestational diabetes: the Toronto Tri-Hospital Gestational Diabetes Project . Am J Obstet Gynecol. 1995 ; 173 ( 1 ): 146 - 156 .

Berggren EK , Boggess KA , Stuebe AM , Jonsson Funk M . National Diabetes Data Group vs Carpenter-Coustan criteria to diagnose gestational diabetes . Am J Obstet Gynecol. 2011 ; 205 ( 3 ): 253 e1 - 253.e7 .

Cheng YW , Block-Kurbisch I , Caughey AB . Carpenter-Coustan criteria compared with the National Diabetes Data Group thresholds for gestational diabetes mellitus . Obstet Gynecol. 2009 ; 114 ( 2 Pt 1 ): 326 - 332 .

Chou CY , Lin CL , Yang CK , et al.  Pregnancy outcomes of Taiwanese women with gestational diabetes mellitus: a comparison of Carpenter-Coustan and National Diabetes Data Group criteria . J Womens Health (Larchmt). 2010 ; 19 ( 5 ): 935 - 939 .

American Diabetes Association . Gestational diabetes mellitus . Diabetes Care. 2003 ; 26 ( suppl 1 ): S103 - S105 .

Metzger BE , Lowe LP , Dyer AR , et al.  ; HAPO Study Cooperative Group. Hyperglycemia and adverse pregnancy outcomes . N Engl J Med. 2008 ; 358 ( 19 ): 1991 - 2002 .

Crowther CA , Hiller JE , Moss JR , et al.  Effect of treatment of gestational diabetes mellitus on pregnancy outcomes . N Engl J Med. 2005 ; 352 ( 24 ): 2477 - 2486 .

Landon MB , Spong CY , Thom E , et al.  A multicenter, randomized trial of treatment for mild gestational diabetes . N Engl J Med. 2009 ; 361 ( 14 ): 1339 - 1348 .

Metzger BE , Gabbe SG , Persson B , et al.  ; International Association of Diabetes and Pregnancy Study Groups Consensus Panel . International Association of Diabetes and Pregnancy Study Groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy . Diabetes Care. 2010 ; 33 ( 3 ): 676 - 682 .

Blumer I , Hadar E , Hadden DR , et al.  Diabetes and pregnancy: an Endocrine Society clinical practice guideline . J Clin Endocrinol Metab. 2013 ; 98 ( 11 ): 4227 - 4249 .

Hod M , Kapur A , Sacks DA , et al.  The International Federation of Gynecology and Obstetrics (FIGO) Initiative on gestational diabetes mellitus: a pragmatic guide for diagnosis, management, and care . Int J Gynaecol Obstet. 2015 ; 131 ( suppl 3 ): S173 - S211 .

Nankervis A , McIntyre HD , Moses R , et al.  ADIPS Consensus Guidelines for the Testing and Diagnosis of Hyperglycaemia in Pregnancy in Australia and New Zealand. Modified November 2014 . http://adips.org/downloads/2014ADIPSGDMGuidelinesV18.11.2014_000.pdf . Accessed June 1, 2021.

Japan Diabetes Society . Evidence-based practice guideline for the treatment for diabetes in Japan 2013 . Last updated November 26, 2020. http://www.jds.or.jp/modules/en/index.php?content_id=44 . Accessed June 1, 2021.

Yang HX . Diagnostic criteria for gestational diabetes mellitus . Chin Med J. 2012 ; 125 ( 7 ): 1212 - 1213 .

Benhalima K , Mathieu C , Damm P , et al.  A proposal for the use of uniform diagnostic criteria for gestational diabetes in Europe: an opinion paper by the European Board & College of Obstetrics and Gynaecology (EBCOG) . Diabetologia. 2015 ; 58 ( 7 ): 1422 - 1429 .

Vandorsten JP , Dodson WC , Espeland MA , et al.  NIH consensus development conference: diagnosing gestational diabetes mellitus . NIH Consens State Sci Statements. 2013 ; 29 ( 1 ): 1 - 31 .

National Institute for Health and Care Excellence . Diabetes in Pregnancy: Management of Diabetes and its Complications from Pre-conception to the Postnatal Period . NICE Clinical Guideline NG3. 2015 .

Vambergue A . Expert consensus on gestational diabetes mellitus . Diabetes Metab. 2010 ; 36 ( 6 Pt 2 ): 511 .

Associazione Medici Diabetologi and Società Italiana di Diabetologia. Italian National Health System Guidelines for the screening of gestational diabetes mellitus . May 28, 2014. http://www.standarditaliani.it/skin/www.standarditaliani.it/pdf/STANDARD_2014_May28.pdf . Accessed June 1, 2021.

American Diabetes Association . 14. Management of diabetes in pregnancy: standards of medical care in diabetes-2020 . Diabetes Care. 2020 ; 43 ( suppl 1 ): S183 - SS92 .

Canadian Diabetes Association Clinical Practice Guidelines Expert Committee . Clinical practice guidelines for the prevention and management of diabetes in Canada . Canadian J Diabetes. 2013 ; 37(suppl 1):S1-S3 .

Kleinwechter H , Schafer-Graf U , Buhrer C , et al.  Gestational diabetes mellitus (GDM) diagnosis, therapy and follow-up care: practice Guideline of the German Diabetes Association (DDG) and the German Association for Gynaecology and Obstetrics (DGGG) . Exp Clin Endocrinol Diabetes. 2014 ; 122 ( 7 ): 395 - 405 .

Seshiah V , Das AK , Balaji V , et al.  Gestational diabetes mellitus—guidelines . J Assoc Physicians India. 2006 ; 54 : 622 - 628 .

World Health Organization . Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications . Report of WHO Consultation. 1999 .

Coustan DR , Lowe LP , Metzger BE , et al.  ; on behalf of the International Association of Diabetes in Pregnancy Study Groups. The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study: paving the way for new diagnostic criteria for gestational diabetes mellitus . Am J Obstet Gynecol. 2010 ; 202 ( 6 ): 654 e1 - 654 e6 .

McIntyre HD , Oats JJ , Kihara AB , et al.  Update on diagnosis of hyperglycemia in pregnancy and gestational diabetes mellitus from FIGO’s Pregnancy & Non-Communicable Diseases Committee . Int J Gynaecol Obstet. 2021; 154 (2):189-194.

Menke A , Casagrande S , Geiss L , Cowie CC . Prevalence of and trends in diabetes among adults in the United States, 1988-2012 . JAMA. 2015 ; 314 ( 10 ): 1021 - 1029 .

Centers for Disease Control and Prevention . National Diabetes Statistics Report, 2020 . US Department of Health and Human Services ; 2020 .

Lieberman N , Kalter-Leibovici O , Hod M . Global adaptation of IADPSG recommendations: a national approach . Int J Gynaecol Obstet. 2011 ; 115 ( suppl 1 ): S45 - S47 .

Marseille E , Lohse N , Jiwani A , et al.  The cost-effectiveness of gestational diabetes screening including prevention of type 2 diabetes: application of a new model in India and Israel . J Matern Fetal Neonatal Med. 2013 ; 26 ( 8 ): 802 - 810 .

Werner EF , Pettker CM , Zuckerwise L , et al.  Screening for gestational diabetes mellitus: are the criteria proposed by the International Association of the Diabetes and Pregnancy Study Groups cost-effective? Diabetes Care . 2012 ; 35 ( 3 ): 529 - 535 .

Mission JF , Ohno MS , Cheng YW , Caughey AB . Gestational diabetes screening with the new IADPSG guidelines: a cost-effectiveness analysis . Am J Obstet Gynecol. 2012 ; 207 ( 4 ): 326 e1 - 326 e9 .

Farrar D , Simmonds M , Griffin S , et al.  The identification and treatment of women with hyperglycaemia in pregnancy: an analysis of individual participant data, systematic reviews, meta-analyses and an economic evaluation . Health Technol Assess. 2016 ; 20 ( 86 ): 1 - 348 .

Jacklin PB , Maresh MJ , Patterson CC , et al.  A cost-effectiveness comparison of the NICE 2015 and WHO 2013 diagnostic criteria for women with gestational diabetes with and without risk factors . BMJ Open. 2017 ; 7 ( 8 ): e016621 .

Cundy T , Ackermann E , Ryan EA . Gestational diabetes: new criteria may triple the prevalence but effect on outcomes is unclear . BMJ. 2014 ; 348 : g1567 .

O’Sullivan EP , Avalos G , O’Reilly M , et al.  Atlantic Diabetes in Pregnancy (DIP): the prevalence and outcomes of gestational diabetes mellitus using new diagnostic criteria . Diabetologia . 2011 ; 54 ( 7 ): 1670 -167 5 .

Lapolla A , Dalfra MG , Ragazzi E , et al.  New International Association of the Diabetes and Pregnancy Study Groups (IADPSG) recommendations for diagnosing gestational diabetes compared with former criteria: a retrospective study on pregnancy outcome . Diabet Med. 2011 ; 28 ( 9 ): 1074 - 1077 .

Benhalima K , Hanssens M , Devlieger R , et al.  Analysis of pregnancy outcomes using the new IADPSG recommendation compared with the Carpenter and Coustan criteria in an area with a low prevalence of gestational diabetes . Int J Endocrinol. 2013 ; 2013 : 248121 .

Hung TH , Hsieh TT . The effects of implementing the International Association of Diabetes and Pregnancy Study Groups criteria for diagnosing gestational diabetes on maternal and neonatal outcomes . PLoS One. 2015 ; 10 ( 3 ): e0122261 .

Meek CL , Lewis HB , Patient C , et al.  Diagnosis of gestational diabetes mellitus: falling through the net . Diabetologia. 2015 ; 58 ( 9 ): 2003 - 2012 .

Djelmis J , Pavic M , Mulliqi Kotori V , et al.  Prevalence of gestational diabetes mellitus according to IADPSG and NICE criteria . Int J Gynaecol Obstet. 2016 ; 135 ( 3 ): 250 - 254 .

Ethridge JK Jr , Catalano PM , Waters TP . Perinatal outcomes associated with the diagnosis of gestational diabetes made by the international association of the diabetes and pregnancy study groups criteria . Obstet Gynecol. 2014 ; 124 ( 3 ): 571 - 578 .

Hillier TA , Pedula KL , Ogasawara KK , et al.  A pragmatic, randomized clinical trial of gestational diabetes screening . N Engl J Med. 2021 ; 384 ( 10 ): 895 - 904 .

Casey B . Gestational diabetes—on broadening the diagnosis . N Engl J Med. 2021 ; 384 ( 10 ): 965 - 966 .

Dunne F , Lindsay R , Loeken M . This is the decade to find the solution for gestational diabetes mellitus screening and treatments . Diabet Med. 2021 ; 38 ( 8 ): e14602 .

O’Malley EG , Reynolds CME , O’Kelly R , et al.  The diagnosis of gestational diabetes mellitus (GDM) using a 75 g oral glucose tolerance test: a prospective observational study . Diabetes Res Clin Pract . 2020 ; 163 : 108144 .

van Leeuwen M , Louwerse MD , Opmeer BC , et al.  Glucose challenge test for detecting gestational diabetes mellitus: a systematic review . BJOG. 2012 ; 119 ( 4 ): 393 - 401 .

Sacks DA , Hadden DR , Maresh M , et al.  Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study . Diabetes Care. 2012 ; 35 ( 3 ): 526 - 528 .

Roeckner JT , Sanchez-Ramos L , Jijon-Knupp R , et al.  Single abnormal value on 3-hour oral glucose tolerance test during pregnancy is associated with adverse maternal and neonatal outcomes: a systematic review and metaanalysis . Am J Obstet Gynecol. 2016 ; 215 ( 3 ): 287 - 297 .

Freinkel N , Metzger BE , Phelps RL , et al.  Gestational diabetes mellitus: heterogeneity of maternal age, weight, insulin secretion, HLA antigens, and islet cell antibodies and the impact of maternal metabolism on pancreatic B-cell and somatic development in the offspring . Diabetes. 1985 ; 34 ( suppl 2 ): 1 - 7 .

Schaefer UM , Songster G , Xiang A , et al.  Congenital malformations in offspring of women with hyperglycemia first detected during pregnancy . Am J Obstet Gynecol. 1997 ; 177 ( 5 ): 1165 - 1171 .

Omori Y , Jovanovic L . Proposal for the reconsideration of the definition of gestational diabetes . Diabetes Care. 2005 ; 28 ( 10 ): 2592 - 2593 .

Stacey T , Tennant P , McCowan L , et al.  Gestational diabetes and the risk of late stillbirth: a case-control study from England, UK . BJOG. 2019 ; 126 ( 8 ): 973 - 982 .

Cosson E , Bentounes SA , Nachtergaele C , et al.  Prognosis associated with sub-types of hyperglycaemia in pregnancy . J Clin Med. 2021 ; 10 ( 17 ): 3904 .

Wong T , Ross GP , Jalaludin BB , Flack JR . The clinical significance of overt diabetes in pregnancy . Diabet Med. 2013 ; 30 ( 4 ): 468 - 474 .

Bartha JL , Martinez-Del-Fresno P , Comino-Delgado R . Early diagnosis of gestational diabetes mellitus and prevention of diabetes-related complications . Eur J Obstet Gynecol Reprod Biol. 2003 ; 109 ( 1 ): 41 - 44 .

Bartha JL , Martinez-Del-Fresno P , Comino-Delgado R . Gestational diabetes mellitus diagnosed during early pregnancy . Am J Obstet Gynecol. 2000 ; 182 ( 2 ): 346 - 350 .

Berkowitz GS , Roman SH , Lapinski RH , et al.  Maternal characteristics, neonatal outcome, and the time of diagnosis of gestational diabetes . Am J Obstet Gynecol. 1992 ; 167 ( 4 Pt 1 ): 976 - 8282 .

Meyer WJ , Carbone J , Gauthier DW , et al.  Early gestational glucose screening and gestational diabetes . J Reprod Med. 1996 ; 41 ( 9 ): 675 - 679 .

Sweeting AN , Ross GP , Hyett J , et al.  Gestational diabetes mellitus in early pregnancy: evidence for poor pregnancy outcomes despite treatment . Diabetes Care. 2016 ; 39 ( 1 ): 75 - 81 .

Li M , Hinkle SN , Grantz KL , et al.  Glycaemic status during pregnancy and longitudinal measures of fetal growth in a multi-racial US population: a prospective cohort study . Lancet Diabetes Endocrinol. 2020 ; 8 ( 4 ): 292 - 300 .

Sovio U , Murphy HR , Smith GC . Accelerated fetal growth prior to diagnosis of gestational diabetes mellitus: a prospective cohort study of nulliparous women . Diabetes Care. 2016 ; 39 ( 6 ): 982 - 987 .

Venkataraman H , Ram U , Craik S , et al.  Increased fetal adiposity prior to diagnosis of gestational diabetes in South Asians: more evidence for the “thin-fat” baby . Diabetologia. 2017 ; 60 ( 3 ): 399 - 405 .

Riskin-Mashiah S , Damti A , Younes G , et al.  Normal fasting plasma glucose levels during pregnancy: a hospital-based study . J Perinat Med. 2011 ; 39 ( 2 ): 209 - 211 .

Mills JL , Jovanovic L , Knopp R , et al.  Physiological reduction in fasting plasma glucose concentration in the first trimester of normal pregnancy: the diabetes in early pregnancy study . Metabolism. 1998 ; 47 ( 9 ): 1140 - 1144 .

Corrado F , D’Anna R , Cannata ML , et al.  Correspondence between first-trimester fasting glycaemia, and oral glucose tolerance test in gestational diabetes diagnosis . Diabetes Metab . 2012 ; 38 ( 5 ): 458 - 61 .

Zhu WW , Yang HX , Wei YM , et al.  Evaluation of the value of fasting plasma glucose in the first prenatal visit to diagnose gestational diabetes mellitus in china . Diabetes Care. 2013 ; 36 ( 3 ): 586 - 590 .

McIntyre HD , Sacks DA , Barbour LA , et al.  Issues with the diagnosis and classification of hyperglycemia in early pregnancy . Diabetes Care. 2016 ; 39 ( 1 ): 53 - 54 .

Hughes RC , Moore MP , Gullam JE , et al.  An early pregnancy HbA1c ≤5.9% (41 mmol/mol) is optimal for detecting diabetes and identifies women at increased risk of adverse pregnancy outcomes . Diabetes Care. 2014 ; 37 ( 11 ): 2953 - 2959 .

Sweeting AN , Ross GP , Hyett J , et al.  Baseline HbA1c to identify high-risk gestational diabetes: utility in early vs standard gestational diabetes . J Clin Endocrinol Metab. 2017 ; 102 ( 1 ): 150 - 156 .

Immanuel J , Simmons D , Desoye G , et al.  Performance of early pregnancy HbA1c for predicting gestational diabetes mellitus and adverse pregnancy outcomes in obese European women . Diabetes Res Clin Pract. 2020 ; 168 : 108378 .

Osmundson SS , Norton ME , El-Sayed YY , et al.  Early screening and treatment of women with prediabetes: a randomized controlled trial . Am J Perinatol. 2016 ; 33 ( 2 ): 172 - 179 .

Roeder HA , Moore TR , Wolfson T , Ramos GA . Treating hyperglycemia in the first trimester: a randomized controlled trial . Am J Obstet Gynecol MFM. 2017 ; 1 ( 1 ): 33 - 41 .

Hawkins JS , Lo JY , Casey BM , et al.  Diet-treated gestational diabetes mellitus: comparison of early vs routine diagnosis . Am J Obstet Gynecol. 2008 ; 198 ( 3 ): 287 e1 - 287 e6 .

Most OL , Kim JH , Arslan AA , et al.  Maternal and neonatal outcomes in early glucose tolerance testing in an obstetric population in New York city . J Perinat Med. 2009 ; 37 ( 2 ): 114 - 117 .

Gupta S , Dolin C , Jadhav A , et al.  Obstetrical outcomes in patients with early onset gestational diabetes . J Matern Fetal Neonatal Med. 2016 ; 29 ( 1 ): 27 - 31 .

Harreiter J , Simmons D , Desoye G , et al.  IADPSG and WHO 2013 gestational diabetes mellitus criteria identify obese women with marked insulin resistance in early pregnancy . Diabetes Care. 2016 ; 39 ( 7 ): e90 - e92 .

Egan AM , Vellinga A , Harreiter J , et al.  Epidemiology of gestational diabetes mellitus according to IADPSG/WHO 2013 criteria among obese pregnant women in Europe . Diabetologia. 2017 ; 60 ( 10 ): 1913 - 1921 .

Immanuel J , Simmons D . Screening and treatment for early-onset gestational diabetes mellitus: a systematic review and meta-analysis . Curr Diab Rep. 2017 ; 17 ( 11 ): 115 .

Bozkurt L , Gobl CS , Pfligl L , et al.  Pathophysiological characteristics and effects of obesity in women with early and late manifestation of gestational diabetes diagnosed by the International Association of Diabetes and Pregnancy Study Groups criteria . J Clin Endocrinol Metab. 2015 ; 100 ( 3 ): 1113 - 1120 .

Bozkurt L , Gobl CS , Hormayer AT , et al.  The impact of preconceptional obesity on trajectories of maternal lipids during gestation . Sci Rep. 2016 ; 6 : 29971 .

Sweeting AN , Ross GP , Hyett J , et al.  Gestational diabetes in the first trimester: is early testing justified? Lancet Diabetes Endocrinol. 2017 ; 5 ( 8 ): 571 - 573 .

Harper LM , Jauk V , Longo S , et al.  Early gestational diabetes screening in obese women: a randomized controlled trial . Am J Obstet Gynecol. 2020 ; 222 ( 5 ): 495 e1 - 495 e8 .

Vinter CA , Tanvig MH , Christensen MH , et al.  Lifestyle intervention in Danish obese pregnant women with early gestational diabetes mellitus according to WHO 2013 criteria does not change pregnancy outcomes: results from the LiP (Lifestyle in Pregnancy) study . Diabetes Care. 2018 ; 41 ( 10 ): 2079 - 2085 .

Simmons D , Hague WM , Teede HJ , et al.  Hyperglycaemia in early pregnancy: the Treatment of Booking Gestational Diabetes Mellitus (TOBOGM) study: a randomised controlled trial . Med J Aust. 2018 ; 209 ( 9 ): 405 - 406 .

Bruns DE , Metzger BE , Sacks DB . Diagnosis of gestational diabetes mellitus will be flawed until we can measure glucose . Clin Chem. 2020 ; 66 ( 2 ): 265 - 267 .

Bogdanet D , O’Shea P , Lyons C , et al.  The oral glucose tolerance test-is it time for a change?-a literature review with an emphasis on pregnancy . J Clin Med. 2020 ; 9 ( 11):3451 .

Sacks DB , Arnold M , Bakris GL , et al.  Position statement executive summary: guidelines and recommendations for laboratory analysis in the diagnosis and management of diabetes mellitus . Diabetes Care. 2011 ; 34 ( 6 ): 1419 - 1423 .

Chan AY , Swaminathan R , Cockram CS . Effectiveness of sodium fluoride as a preservative of glucose in blood . Clin Chem. 1989 ; 35 ( 2 ): 315 - 317 .

Potter JM , Hickman PE , Oakman C , et al.  Strict preanalytical oral glucose tolerance test blood sample handling is essential for diagnosing gestational diabetes mellitus . Diabetes Care. 2020 ; 43 ( 7 ): 1438 - 1441 .

Daly N , Flynn I , Carroll C , et al.  Impact of implementing preanalytical laboratory standards on the diagnosis of gestational diabetes mellitus: a prospective observational study . Clin Chem. 2016 ; 62 ( 2 ): 387 - 391 .

International Diabetes Federation . IDF Diabetes Atlas . 9th ed. 2019 .

Guariguata L , Linnenkamp U , Beagley J , et al.  Global estimates of the prevalence of hyperglycaemia in pregnancy . Diabetes Res Clin Pract. 2014 ; 103 ( 2 ): 176 - 185 .

Prentice PM , Olga L , Petry CJ , et al.  Reduced size at birth and persisting reductions in adiposity in recent, compared with earlier, cohorts of infants born to mothers with gestational diabetes mellitus . Diabetologia. 2019 ; 62 ( 11 ): 1977 - 1987 .

Hartling L , Dryden DM . Screening and diagnosing gestational diabetes mellitus . Evid Rep Technol Assess (Full Rep). 2012 ; 210 : 1 - 327 .

Bottalico JN . Recurrent gestational diabetes: risk factors, diagnosis, management, and implications . Semin Perinatol. 2007 ; 31 ( 3 ): 176 - 184 .

Anna V , van der Ploeg H , P , Cheung NW , et al.  Sociodemographic correlates of the increasing trend in prevalence of gestational diabetes mellitus in a large population of women between 1995 and 2005 . Diabetes Care. 2008 ; 31 ( 12 ): 2288 - 2293 .

Chu SY , Callaghan WM , Kim SY , et al.  Maternal obesity and risk of gestational diabetes mellitus . Diabetes Care. 2007 ; 30 ( 8 ): 2070 - 2076 .

Torloni MR , Betran AP , Horta BL , et al.  Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis . Obes Rev. 2009 ; 10 ( 2 ): 194 - 203 .

Mokdad AH , Ford ES , Bowman BA , et al.  Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA. 2003 ; 289 ( 1 ): 76 - 79 .

Mulla WR , Henry TQ , Homko CJ . Gestational diabetes screening after HAPO: has anything changed? Curr Diab Rep. 2010 ; 10 ( 3 ): 224 - 228 .

Petry CJ . Gestational diabetes: risk factors and recent advances in its genetics and treatment . Br J Nutr. 2010 ; 104 ( 6 ): 775 - 787 .

Hunt KJ , Schuller KL . The increasing prevalence of diabetes in pregnancy . Obstet Gynecol Clin North Am. 2007 ; 34 ( 2 ): 173 - 199 , vii.

Agarwal MM , Dhatt GS , Shah SM . Gestational diabetes mellitus: simplifying the international association of diabetes and pregnancy diagnostic algorithm using fasting plasma glucose . Diabetes Care. 2010 ; 33 ( 9 ): 2018 - 2020 .

Moses RG , Morris GJ , Petocz P , et al.  The impact of potential new diagnostic criteria on the prevalence of gestational diabetes mellitus in Australia . Med J Aust. 2011 ; 194 ( 7 ): 338 - 340 .

Brown FM , Wyckoff J . Application of one-step IADPSG versus two-step diagnostic criteria for gestational diabetes in the real world: impact on health services, clinical care, and outcomes . Curr Diab Rep. 2017 ; 17 ( 10 ): 85 .

Kim C , Berger DK , Chamany S . Recurrence of gestational diabetes mellitus: a systematic review . Diabetes Care. 2007 ; 30 ( 5 ): 1314 - 1319 .

Solomon CG , Willett WC , Carey VJ , et al.  A prospective study of pregravid determinants of gestational diabetes mellitus . JAMA. 1997 ; 278 ( 13 ): 1078 - 1083 .

Chamberlain C , McNamara B , Williams ED , et al.  Diabetes in pregnancy among indigenous women in Australia, Canada, New Zealand and the United States . Diabetes Metab Res Rev. 2013 ; 29 ( 4 ): 241 - 256 .

Hedderson MM , Gunderson EP , Ferrara A . Gestational weight gain and risk of gestational diabetes mellitus . Obstet Gynecol. 2010 ; 115 ( 3 ): 597 - 604 .

Kjos SL , Buchanan TA . Gestational diabetes mellitus . N Engl J Med. 1999 ; 341 ( 23 ): 1749 - 1756 .

Di Cianni G , Volpe L , Lencioni C , et al.  Prevalence and risk factors for gestational diabetes assessed by universal screening . Diabetes Res Clin Pract. 2003 ; 62 ( 2 ): 131 - 137 .

Cypryk K , Szymczak W , Czupryniak L , et al.  Gestational diabetes mellitus—an analysis of risk factors . Endokrynol Pol. 2008 ; 59 ( 5 ): 393 - 397 .

Cleary-Goldman J , Malone FD , Vidaver J , et al.  Impact of maternal age on obstetric outcome . Obstet Gynecol. 2005 ; 105 ( 5 Pt 1 ): 983 - 990 .

Yang H , Wei Y , Gao X , et al.  Risk factors for gestational diabetes mellitus in Chinese women: a prospective study of 16,286 pregnant women in China . Diabet Med. 2009 ; 26 ( 11 ): 1099 - 1104 .

Morisset AS , St-Yves A , Veillette J , et al.  Prevention of gestational diabetes mellitus: a review of studies on weight management . Diabetes Metab Res Rev. 2010 ; 26 ( 1 ): 17 - 25 .

Weiss JL , Malone FD , Emig D , et al.  Obesity, obstetric complications and cesarean delivery rate—a population-based screening study . Am J Obstet Gynecol. 2004 ; 190 ( 4 ): 1091 - 1097 .

Gibson KS , Waters TP , Catalano PM . Maternal weight gain in women who develop gestational diabetes mellitus . Obstet Gynecol. 2012 ; 119 ( 3 ): 560 - 565 .

Morisset AS , Tchernof A , Dube MC , et al.  Weight gain measures in women with gestational diabetes mellitus . J Womens Health (Larchmt). 2011 ; 20 ( 3 ): 375 - 380 .

Tovar A , Must A , Bermudez OI , et al.  The impact of gestational weight gain and diet on abnormal glucose tolerance during pregnancy in Hispanic women . Matern Child Health J. 2009 ; 13 ( 4 ): 520 - 530 .

Teulings N , Masconi KL , Ozanne SE , et al.  Effect of interpregnancy weight change on perinatal outcomes: systematic review and meta-analysis . BMC Pregnancy Childbirth. 2019 ; 19 ( 1 ): 386 .

Lo JC , Feigenbaum SL , Escobar GJ , et al.  Increased prevalence of gestational diabetes mellitus among women with diagnosed polycystic ovary syndrome: a population-based study . Diabetes Care. 2006 ; 29 ( 8 ): 1915 - 1917 .

Mikola M , Hiilesmaa V , Halttunen M , et al.  Obstetric outcome in women with polycystic ovarian syndrome . Hum Reprod. 2001 ; 16 ( 2 ): 226 - 229 .

Dinham GK , Henry A , Lowe SA , et al.  Twin pregnancies complicated by gestational diabetes mellitus: a single centre cohort study . Diabet Med. 2016 ; 33 ( 12 ): 1659 - 1667 .

Rauh-Hain JA , Rana S , Tamez H , et al.  Risk for developing gestational diabetes in women with twin pregnancies . J Matern Fetal Neonatal Med. 2009 ; 22 ( 4 ): 293 - 299 .

Chasan-Taber L , Schmidt MD , Pekow P , et al.  Physical activity and gestational diabetes mellitus among Hispanic women . J Womens Health (Larchmt). 2008 ; 17 ( 6 ): 999 - 1008 .

Mottola MF . The role of exercise in the prevention and treatment of gestational diabetes mellitus . Curr Diab Rep. 2008 ; 8 ( 4 ): 299 - 304 .

Zhang C , Liu S , Solomon CG , et al.  Dietary fiber intake, dietary glycemic load, and the risk for gestational diabetes mellitus . Diabetes Care. 2006 ; 29 ( 10 ): 2223 - 2230 .

Kucukgoncu S , Guloksuz S , Celik K , et al.  Antipsychotic exposure in pregnancy and the risk of gestational diabetes: a systematic review and meta-analysis . Schizophr Bull. 2020 ; 46 ( 2 ): 311 - 318 .

Galbally M , Frayne J , Watson SJ , et al.  The association between gestational diabetes mellitus, antipsychotics and severe mental illness in pregnancy: a multicentre study . Aust N Z J Obstet Gynaecol. 2020 ; 60 ( 1 ): 63 - 69 .

Hedderson MM , Ferrara A . High blood pressure before and during early pregnancy is associated with an increased risk of gestational diabetes mellitus . Diabetes Care. 2008 ; 31 ( 12 ): 2362 - 2367 .

Lao TT , Ho LF . First-trimester blood pressure and gestational diabetes in high-risk Chinese women . J Soc Gynecol Investig. 2003 ; 10 ( 2 ): 94 - 98 .

Sweeting AN , Appelblom H , Ross GP , et al.  First trimester prediction of gestational diabetes mellitus: a clinical model based on maternal demographic parameters . Diabetes Res Clin Pract. 2017 ; 127 : 44 - 50 .

Syngelaki A , Visser GH , Krithinakis K , et al.  First trimester screening for gestational diabetes mellitus by maternal factors and markers of inflammation . Metabolism. 2016 ; 65 ( 3 ): 131 - 137 .

Nanda S , Savvidou M , Syngelaki A , et al.  Prediction of gestational diabetes mellitus by maternal factors and biomarkers at 11 to 13 weeks . Prenat Diagn. 2011 ; 31 ( 2 ): 135 - 141 .

van Leeuwen M , Opmeer BC , Zweers EJ , et al.  Estimating the risk of gestational diabetes mellitus: a clinical prediction model based on patient characteristics and medical history . BJOG. 2010 ; 117 ( 1 ): 69 - 75 .

Naylor CD , Sermer M , Chen E , et al.  ; Toronto Trihospital Gestational Diabetes Project Investigators. Selective screening for gestational diabetes mellitus . N Engl J Med. 1997 ; 337 ( 22 ): 1591 - 1596 .

Catalano PM . Trying to understand gestational diabetes . Diabet Med. 2014 ; 31 ( 3 ): 273 - 281 .

Catalano PM . Carbohydrate metabolism and gestational diabetes . Clin Obstet Gynecol. 1994 ; 37 ( 1 ): 25 - 38 .

Lain KY , Catalano PM . Metabolic changes in pregnancy . Clin Obstet Gynecol. 2007 ; 50 ( 4 ): 938 - 948 .

Costrini NV , Kalkhoff RK . Relative effects of pregnancy, estradiol, and progesterone on plasma insulin and pancreatic islet insulin secretion . J Clin Invest. 1971 ; 50 ( 5 ): 992 - 999 .

Kalkhoff RK , Kissebah AH , Kim HJ . Carbohydrate and lipid metabolism during normal pregnancy: relationship to gestational hormone action . Semin Perinatol. 1978 ; 2 ( 4 ): 291 - 307 .

Leturque A , Hauguel S , Sutter Dub MT , et al.  Effects of placental lactogen and progesterone on insulin stimulated glucose metabolism in rat muscles in vitro . Diabete Metab. 1989 ; 15 ( 4 ): 176 - 181 .

Ryan EA , Enns L . Role of gestational hormones in the induction of insulin resistance . J Clin Endocrinol Metab. 1988 ; 67 ( 2 ): 341 - 347 .

Alvarez JJ , Montelongo A , Iglesias A , Lasuncion MA , Herrera E . Longitudinal study on lipoprotein profile, high density lipoprotein subclass, and postheparin lipases during gestation in women . J Lipid Res. 1996 ; 37 ( 2 ): 299 - 308 .

Barbour LA , McCurdy CE , Hernandez TL , Kirwan JP , Catalano PM , Friedman JE . Cellular mechanisms for insulin resistance in normal pregnancy and gestational diabetes . Diabetes Care. 2007 ; 30 ( suppl 2 ): S112 - S119 .

Bomba-Opon D , Wielgos M , Szymanska M , et al.  Effects of free fatty acids on the course of gestational diabetes mellitus . Neuro Endocrinol Lett. 2006 ; 27 ( 1-2 ): 277 - 280 .

Boden G , Chen X , Ruiz J , et al.  Mechanisms of fatty acid-induced inhibition of glucose uptake . J Clin Invest. 1994 ; 93 ( 6 ): 2438 - 2446 .

Catalano PM , Huston L , Amini SB , et al.  Longitudinal changes in glucose metabolism during pregnancy in obese women with normal glucose tolerance and gestational diabetes mellitus . Am J Obstet Gynecol. 1999 ; 180 ( 4 ): 903 - 916 .

Buchanan TA , Metzger BE , Freinkel N , et al.  Insulin sensitivity and B-cell responsiveness to glucose during late pregnancy in lean and moderately obese women with normal glucose tolerance or mild gestational diabetes . Am J Obstet Gynecol. 1990 ; 162 ( 4 ): 1008 - 1014 .

Sivan E , Chen X , Homko CJ , et al.  Longitudinal study of carbohydrate metabolism in healthy obese pregnant women . Diabetes Care. 1997 ; 20 ( 9 ): 1470 - 1475 .

Caro JF , Dohm LG , Pories WJ , et al.  Cellular alterations in liver, skeletal muscle, and adipose tissue responsible for insulin resistance in obesity and type II diabetes . Diabetes Metab Rev. 1989 ; 5 ( 8 ): 665 - 689 .

Freinkel N , Metzger BE , Nitzan M , et al.  Faciltated anabolism in late pregnancy: some novel maternal compensations for accelerated starvation. In: Malaise WJ , Pirart J , Vallence-Own J , eds. International Congress Series 312 . Excerpta Medica ; 1974 : 474 - 488 .

Yogev Y , Ben-Haroush A , Chen R , et al.  Diurnal glycemic profile in obese and normal weight nondiabetic pregnant women . Am J Obstet Gynecol. 2004 ; 191 ( 3 ): 949 - 953 .

Hernandez TL , Friedman JE , Van Pelt RE , et al.  Patterns of glycemia in normal pregnancy: should the current therapeutic targets be challenged? Diabetes Care. 2011 ; 34 ( 7 ): 1660 - 1668 .

Kuhl C . Etiology and pathogenesis of gestational diabetes . Diabetes Care. 1998 ; 21 ( suppl 2 ): B19 - B26 .

Parsons JA , Brelje TC , Sorenson RL . Adaptation of islets of Langerhans to pregnancy: increased islet cell proliferation and insulin secretion correlates with the onset of placental lactogen secretion . Endocrinology. 1992 ; 130 ( 3 ): 1459 - 1466 .

Sorenson RL , Brelje TC . Adaptation of islets of Langerhans to pregnancy: beta-cell growth, enhanced insulin secretion and the role of lactogenic hormones . Horm Metab Res. 1997 ; 29 ( 6 ): 301 - 307 .

Nielsen JH , Galsgaard ED , Moldrup A , et al.  Regulation of beta-cell mass by hormones and growth factors . Diabetes. 2001 ; 50 ( suppl 1 ): S25 - S29 .

Parsons JA , Bartke A , Sorenson RL . Number and size of islets of Langerhans in pregnant, human growth hormone-expressing transgenic, and pituitary dwarf mice: effect of lactogenic hormones . Endocrinology. 1995 ; 136 ( 5 ): 2013 - 2021 .

Rieck S , White P , Schug J , et al.  The transcriptional response of the islet to pregnancy in mice . Mol Endocrinol. 2009 ; 23 ( 10 ): 1702 - 1712 .

Buchanan TA , Kitzmiller JL . Metabolic interactions of diabetes and pregnancy . Annu Rev Med. 1994 ; 45 : 245 - 260 .

Rieck S , Kaestner KH . Expansion of beta-cell mass in response to pregnancy . Trends Endocrinol Metab. 2010 ; 21 ( 3 ): 151 - 158 .

Koukkou E , Watts GF , Lowy C . Serum lipid, lipoprotein and apolipoprotein changes in gestational diabetes mellitus: a cross-sectional and prospective study . J Clin Pathol. 1996 ; 49 ( 8 ): 634 - 637 .

Nolan CJ . Controversies in gestational diabetes . Best Pract Res Clin Obstet Gynaecol. 2011 ; 25 ( 1 ): 37 - 49 .

Talchai C , Xuan S , Lin HV , Sussel L , Accili D . Pancreatic beta cell dedifferentiation as a mechanism of diabetic beta cell failure . Cell. 2012 ; 150 ( 6 ): 1223 - 1234 .

Halban PA , Polonsky KS , Bowden DW , et al.  beta-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment . Diabetes Care. 2014 ; 37 ( 6 ): 1751 - 1758 .

Catalano PM , Tyzbir ED , Wolfe RR , et al.  Carbohydrate metabolism during pregnancy in control subjects and women with gestational diabetes . Am J Physiol. 1993 ; 264 ( 1 Pt 1 ): E60 - E67 .

Homko C , Sivan E , Chen X , et al.  Insulin secretion during and after pregnancy in patients with gestational diabetes mellitus . J Clin Endocrinol Metab. 2001 ; 86 ( 2 ): 568 - 573 .

Powe CE , Huston Presley LP , Locascio JJ , et al.  Augmented insulin secretory response in early pregnancy . Diabetologia. 2019 ; 62 ( 8 ): 1445 - 1452 .

Dluski DF , Wolińska E , Skrzypczak M . Epigenetic changes in gestational diabetes mellitus . Int J Mol Sci. 2021 ; 22 ( 14 ):7649.

Elliott HR , Sharp GC , Relton CL , et al.  Epigenetics and gestational diabetes: a review of epigenetic epidemiology studies and their use to explore epigenetic mediation and improve prediction . Diabetologia. 2019 ; 62 ( 12 ): 2171 - 2178 .

Hayes MG , Urbanek M , Hivert MF , et al.  Identification of HKDC1 and BACE2 as genes influencing glycemic traits during pregnancy through genome-wide association studies . Diabetes. 2013 ; 62 ( 9 ): 3282 - 3291 .

Liu S , Liu Y , Liao S . Heterogeneous impact of type 2 diabetes mellitus-related genetic variants on gestational glycemic traits: review and future research needs . Mol Genet Genomics. 2019 ; 294 ( 4 ): 811 - 847 .

Wu L , Cui L , Tam WH , et al.  Genetic variants associated with gestational diabetes mellitus: a meta-analysis and subgroup analysis . Sci Rep. 2016 ; 6 : 30539 .

Chon SJ , Kim SY , Cho NR , et al.  Association of variants in PPARgamma(2), IGF2BP2, and KCNQ1 with a susceptibility to gestational diabetes mellitus in a Korean population . Yonsei Med J. 2013 ; 54 ( 2 ): 352 - 357 .

Kim JY , Cheong HS , Park BL , et al.  Melatonin receptor 1 B polymorphisms associated with the risk of gestational diabetes mellitus . BMC Med Genet. 2011 ; 12 : 82 .

Klein K , Haslinger P , Bancher-Todesca D , et al.  Transcription factor 7-like 2 gene polymorphisms and gestational diabetes mellitus . J Matern Fetal Neonatal Med. 2012 ; 25 ( 9 ): 1783 - 1786 .

Alharbi KK , Khan IA , Abotalib Z , et al.  Insulin receptor substrate-1 (IRS-1) Gly927Arg: correlation with gestational diabetes mellitus in Saudi women . Biomed Res Int. 2014 ; 2014 : 146495 .

Tok EC , Ertunc D , Bilgin O , et al.  PPAR-gamma2 Pro12Ala polymorphism is associated with weight gain in women with gestational diabetes mellitus . Eur J Obstet Gynecol Reprod Biol. 2006 ; 129 ( 1 ): 25 - 30 .

Montazeri S , Nalliah S , Radhakrishnan AK . Is there a genetic variation association in the IL-10 and TNF alpha promoter gene with gestational diabetes mellitus? Hereditas. 2010 ; 147 ( 2 ): 94 - 102 .

Kwak SH , Kim SH , Cho YM , et al.  A genome-wide association study of gestational diabetes mellitus in Korean women . Diabetes. 2012 ; 61 ( 2 ): 531 - 541 .

Lyssenko V , Nagorny CL , Erdos MR , et al.  Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion . Nat Genet. 2009 ; 41 ( 1 ): 82 - 88 .

Wellcome Trust Case Control Consortium . Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls . Nature. 2007 ; 447 ( 7145 ): 661 - 678 .

Bouatia-Naji N , Bonnefond A , Cavalcanti-Proenca C , et al.  A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk . Nat Genet. 2009 ; 41 ( 1 ): 89 - 94 .

Saxena R , Elbers CC , Guo Y , et al.  Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci . Am J Hum Genet. 2012 ; 90 ( 3 ): 410 - 425 .

Manning AK , Hivert MF , Scott RA , et al.  A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance . Nat Genet. 2012 ; 44 ( 6 ): 659 - 669 .

Sparso T , Andersen G , Nielsen T , et al.  The GCKR rs780094 polymorphism is associated with elevated fasting serum triacylglycerol, reduced fasting and OGTT-related insulinaemia, and reduced risk of type 2 diabetes . Diabetologia. 2008 ; 51 ( 1 ): 70 - 75 .

Vaxillaire M , Cavalcanti-Proenca C , Dechaume A , et al.  The common P446L polymorphism in GCKR inversely modulates fasting glucose and triglyceride levels and reduces type 2 diabetes risk in the DESIR prospective general French population . Diabetes. 2008 ; 57 ( 8 ): 2253 - 2257 .

Agius L . Glucokinase and molecular aspects of liver glycogen metabolism . Biochem J. 2008 ; 414 ( 1 ): 1 - 18 .

Chasman DI , Pare G , Mora S , et al.  Forty-three loci associated with plasma lipoprotein size, concentration, and cholesterol content in genome-wide analysis . PLoS Genet. 2009 ; 5 ( 11 ): e1000730 .

Seidah NG . The proprotein convertases, 20 years later . Methods Mol Biol. 2011 ; 768 : 23 - 57 .

Benzinou M , Creemers JW , Choquet H , et al.  Common nonsynonymous variants in PCSK1 confer risk of obesity . Nat Genet. 2008 ; 40 ( 8 ): 943 - 945 .

Hayes A , Chevalier A , D’Souza M , et al.  Early childhood obesity: association with healthcare expenditure in Australia . Obesity (Silver Spring) . 2016 ; 24 ( 8 ): 1752 - 8 .

Powe CE , Nodzenski M , Talbot O , et al.  Genetic determinants of glycemic traits and the risk of gestational diabetes mellitus . Diabetes. 2018 ; 67 ( 12 ): 2703 - 2709 .

Fajans SS , Bell GI , Polonsky KS . Molecular mechanisms and clinical pathophysiology of maturity-onset diabetes of the young . N Engl J Med. 2001 ; 345 ( 13 ): 971 - 980 .

Hattersley AT , Patel KA . Precision diabetes: learning from monogenic diabetes . Diabetologia. 2017 ; 60 ( 5 ): 769 - 777 .

Dickens LT , Naylor RN . Clinical management of women with monogenic diabetes during pregnancy . Curr Diab Rep. 2018 ; 18 ( 3 ): 12 .

Chakera AJ , Steele AM , Gloyn AL , et al.  Recognition and management of individuals with hyperglycemia because of a heterozygous glucokinase mutation . Diabetes Care. 2015 ; 38 ( 7 ): 1383 - 1392 .

Urbanova J , Brunerova L , Nunes M , et al.  Identification of MODY among patients screened for gestational diabetes: a clinician’s guide . Arch Gynecol Obstet. 2020 ; 302 ( 2 ): 305 - 314 .

Wang Z , Ping F , Zhang Q , et al.  Preliminary screening of mutations in the glucokinase gene of Chinese patients with gestational diabetes . J Diabetes Investig. 2018 ; 9 ( 1 ): 199 - 203 .

Gjesing AP , Rui G , Lauenborg J , et al.  High prevalence of diabetes-predisposing variants in MODY genes among Danish women with gestational diabetes mellitus . J Endocr Soc. 2017 ; 1 ( 6 ): 681 - 690 .

Shields BM , Hicks S , Shepherd MH , et al.  Maturity-onset diabetes of the young (MODY): how many cases are we missing? Diabetologia. 2010 ; 53 ( 12 ): 2504 - 2508 .

Spyer G , Hattersley AT , Sykes JE , et al.  Influence of maternal and fetal glucokinase mutations in gestational diabetes . Am J Obstet Gynecol. 2001 ; 185 ( 1 ): 240 - 241 .

Hattersley AT , Beards F , Ballantyne E , et al.  Mutations in the glucokinase gene of the fetus result in reduced birth weight . Nat Genet. 1998 ; 19 ( 3 ): 268 - 270 .

Scholtens DM , Kuang A , Lowe LP , et al.  Hyperglycemia and adverse pregnancy outcome follow-up study (HAPO FUS): maternal glycemia and childhood glucose metabolism . Diabetes Care. 2019 ; 42 ( 3 ): 381 - 392 .

Saravanan P ; Diabetes in Pregnancy Working Group . Gestational diabetes: opportunities for improving maternal and child health . Lancet Diabetes Endocrinol. 2020 ; 8 ( 9 ): 793 - 800 .

Pedersen J . Hyperglycaemia-hyperinsulinism theory and birthweight. In: The Pregnant Diabetic and Her Newborn: Problems and Management . Williams and Wilkins ; 1977 : 211 - 220 .

Illsley NP . Glucose transporters in the human placenta . Placenta. 2000 ; 21 ( 1 ): 14 - 22 .

Pedersen J . Weight and length at birth of infants of diabetic mothers . Acta Endocrinol (Copenh). 1954 ; 16 ( 4 ): 330 - 342 .

Whitelaw A . Subcutaneous fat in newborn infants of diabetic mothers: an indication of quality of diabetic control . Lancet. 1977 ; 1 ( 8001 ): 15 - 18 .

Vrijkotte TG , Krukziener N , Hutten BA , et al.  Maternal lipid profile during early pregnancy and pregnancy complications and outcomes: the ABCD study . J Clin Endocrinol Metab. 2012 ; 97 ( 11 ): 3917 - 3925 .

Yang X , Hsu-Hage B , Zhang H , et al.  Women with impaired glucose tolerance during pregnancy have significantly poor pregnancy outcomes . Diabetes Care. 2002 ; 25 ( 9 ): 1619 - 1624 .

Spellacy WN , Miller S , Winegar A , et al.  Macrosomia—maternal characteristics and infant complications . Obstet Gynecol. 1985 ; 66 ( 2 ): 158 - 161 .

Jang HC , Cho NH , Min YK , et al.  Increased macrosomia and perinatal morbidity independent of maternal obesity and advanced age in Korean women with GDM . Diabetes Care. 1997 ; 20 ( 10 ): 1582 - 1588 .

Langer O , Yogev Y , Most O , et al.  Gestational diabetes: the consequences of not treating . Am J Obstet Gynecol. 2005 ; 192 ( 4 ): 989 - 997 .

Weiss PA , Haeusler M , Tamussino K , et al.  Can glucose tolerance test predict fetal hyperinsulinism? BJOG. 2000 ; 107 ( 12 ): 1480 - 1485 .

Boulet SL , Alexander GR , Salihu HM , et al.  Macrosomic births in the united states: determinants, outcomes, and proposed grades of risk . Am J Obstet Gynecol. 2003 ; 188 ( 5 ): 1372 - 1378 .

Ryckman KK , Spracklen CN , Smith CJ , et al.  Maternal lipid levels during pregnancy and gestational diabetes: a systematic review and meta-analysis . BJOG. 2015 ; 122 ( 5 ): 643 - 651 .

Jovanovic L , Pettitt DJ . Gestational diabetes mellitus . JAMA. 2001 ; 286 ( 20 ): 2516 - 2518 .

Esakoff TF , Cheng YW , Sparks TN , et al.  The association between birthweight 4000 g or greater and perinatal outcomes in patients with and without gestational diabetes mellitus . Am J Obstet Gynecol. 2009 ; 200 ( 6 ): 672 e1 - 672 e4 .

Henriksen T . The macrosomic fetus: a challenge in current obstetrics . Acta Obstet Gynecol Scand. 2008 ; 87 ( 2 ): 134 - 145 .

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

Cetin H , Yalaz M , Akisu M , et al.  Polycythaemia in infants of diabetic mothers: beta-hydroxybutyrate stimulates erythropoietic activity . J Int Med Res. 2011 ; 39 ( 3 ): 815 - 821 .

Farrar D , Fairley L , Santorelli G , et al.  Association between hyperglycaemia and adverse perinatal outcomes in south Asian and white British women: analysis of data from the Born in Bradford cohort . Lancet Diabetes Endocrinol. 2015 ; 3 ( 10 ): 795 - 804 .

Balsells M , Corcoy R , Adelantado JM , et al.  Gestational diabetes mellitus: metabolic control during labour . Diabetes Nutr Metab. 2000 ; 13 ( 5 ): 257 - 262 .

Farrar D , Simmonds M , Bryant M , et al.  Hyperglycaemia and risk of adverse perinatal outcomes: systematic review and meta-analysis . BMJ. 2016 ; 354 : i4694 .

Billionnet C , Mitanchez D , Weill A , et al.  Gestational diabetes and adverse perinatal outcomes from 716,152 births in France in 2012 . Diabetologia. 2017 ; 60 ( 4 ): 636 - 644 .

Allen VM , Armson BA . SOGC Clinical Practice Guideline: teratogenicity associated with pre-existing and gestational diabetes . J Obstet Gynaecol Can. 2007 ; 29 ( 11 ): 927 - 934 .

Owens LA , O’Sullivan EP , Kirwan B , et al.  ATLANTIC DIP: the impact of obesity on pregnancy outcome in glucose-tolerant women . Diabetes Care. 2010 ; 33 ( 3 ): 577 - 579 .

Dennedy MC , Avalos G , O’Reilly MW , et al.  ATLANTIC-DIP: raised maternal body mass index (BMI) adversely affects maternal and fetal outcomes in glucose-tolerant women according to International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria . J Clin Endocrinol Metab. 2012 ; 97 ( 4 ): E608 - E612 .

Rosenstein MG , Cheng YW , Snowden JM , et al.  The risk of stillbirth and infant death stratified by gestational age in women with gestational diabetes . Am J Obstet Gynecol. 2012 ; 206 ( 4 ): 309 e1 - 309 e7 .

Mitanchez D . Foetal and neonatal complications in gestational diabetes: perinatal mortality, congenital malformations, macrosomia, shoulder dystocia, birth injuries, neonatal complications . Diabetes Metab. 2010 ; 36 ( 6 Pt 2 ): 617 - 627 .

Cundy T , Gamble G , Townend K , et al.  Perinatal mortality in type 2 diabetes mellitus . Diabet Med. 2000 ; 17 ( 1 ): 33 - 39 .

Hutcheon JA , Kuret V , Joseph KS , et al.  Immortal time bias in the study of stillbirth risk factors: the example of gestational diabetes . Epidemiology. 2013 ; 24 ( 6 ): 787 - 790 .

Poston L , Bell R , Croker H , et al.  Effect of a behavioural intervention in obese pregnant women (the UPBEAT study): a multicentre, randomised controlled trial . Lancet Diabetes Endocrinol. 2015 ; 3 ( 10 ): 767 - 777 .

Tam WH , Ma RCW , Ozaki R , et al.  In utero exposure to maternal hyperglycemia increases childhood cardiometabolic risk in Offspring . Diabetes Care. 2017 ; 40 ( 5 ): 679 - 686 .

Yu Y , Arah OA , Liew Z , et al.  Maternal diabetes during pregnancy and early onset of cardiovascular disease in offspring: population based cohort study with 40 years of follow-up . BMJ. 2019 ; 367 : l6398 .

Hod M . The fetal heart in gestational diabetes: long-term effects . BJOG. 2021 ; 128 ( 2 ): 280 .

Dabelea D , Hanson RL , Lindsay RS , et al.  Intrauterine exposure to diabetes conveys risks for type 2 diabetes and obesity: a study of discordant sibships . Diabetes. 2000 ; 49 ( 12 ): 2208 - 2211 .

Lowe WL Jr. , Lowe LP , Kuang A , et al.  Maternal glucose levels during pregnancy and childhood adiposity in the Hyperglycemia and Adverse Pregnancy Outcome Follow-up Study . Diabetologia. 2019 ; 62 ( 4 ): 598 - 610 .

Page KA , Luo S , Wang X , et al.  Children exposed to maternal obesity or gestational diabetes mellitus during early fetal development have hypothalamic alterations that predict future weight gain . Diabetes Care. 2019 ; 42 ( 8 ): 1473 - 1480 .

Anderberg E , Kallen K , Berntorp K . The impact of gestational diabetes mellitus on pregnancy outcome comparing different cut-off criteria for abnormal glucose tolerance . Acta Obstet Gynecol Scand. 2010 ; 89 ( 12 ): 1532 - 1537 .

Ju H , Rumbold AR , Willson KJ , et al.  Borderline gestational diabetes mellitus and pregnancy outcomes . BMC Pregnancy Childbirth. 2008 ; 8 : 31 .

Jastrow N , Roberge S , Gauthier RJ , et al.  Effect of birth weight on adverse obstetric outcomes in vaginal birth after cesarean delivery . Obstet Gynecol. 2010 ; 115 ( 2 Pt 1 ): 338 - 343 .

Metzger BE , Coustan DR . Summary and recommendations of the Fourth International Workshop-Conference on Gestational Diabetes Mellitus. The Organizing Committee . Diabetes Care. 1998 ; 21 ( suppl 2 ): B161 - B167 .

Hiden U , Lassance L , Tabrizi NG , et al.  Fetal insulin and IGF-II contribute to gestational diabetes mellitus (GDM)-associated up-regulation of membrane-type matrix metalloproteinase 1 (MT1-MMP) in the human feto-placental endothelium . J Clin Endocrinol Metab. 2012 ; 97 ( 10 ): 3613 - 3621 .

Dunne FP , Avalos G , Durkan M , et al.  ATLANTIC DIP: pregnancy outcomes for women with type 1 and type 2 diabetes . Ir Med J. 2012 ; 105 ( 5 suppl ): 6 - 9 .

Roberts JM , Redman CW . Pre-eclampsia: more than pregnancy-induced hypertension . Lancet. 1993 ; 341 ( 8858 ): 1447 - 1451 .

Desoye G , Hauguel-de Mouzon S . The human placenta in gestational diabetes mellitus. The Insulin and Cytokine Network . Diabetes Care. 2007 ; 30 ( suppl 2 ): S120 - S126 .

Belkacemi L , Lash GE , Macdonald-Goodfellow SK , et al.  Inhibition of human trophoblast invasiveness by high glucose concentrations . J Clin Endocrinol Metab. 2005 ; 90 ( 8 ): 4846 - 4851 .

Vounzoulaki E , Khunti K , Abner SC , et al.  Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis . BMJ. 2020 ; 369 : m1361 .

Daly B , Toulis KA , Thomas N , et al.  Increased risk of ischemic heart disease, hypertension, and type 2 diabetes in women with previous gestational diabetes mellitus, a target group in general practice for preventive interventions: a population-based cohort study. . PLoS Med. 2018 ; 15 ( 1 ): e1002488 .

Lowe WL Jr , Scholtens DM , Kuang A , et al.  Hyperglycemia and adverse pregnancy outcome follow-up study (HAPO FUS): maternal gestational diabetes mellitus and childhood glucose metabolism . Diabetes Care. 2019 ; 42 ( 3 ): 372 - 380 .

Murphy HR . 2020 NICE guideline update: good news for pregnant women with type 1 diabetes and past or current gestational diabetes . Diabet Med. 2021 ; 38 ( 6 ): e14576 .

National Institute for Health and Care Excellence . Diabetes in pregnancy: management of diabetes and its complications from preconception to the postnatal period . NICE Guidelines NG3. 2015 . Last updated December 16, 2020. https://www.nice.org.uk/guidance/ng3

Carr DB , Utzschneider KM , Hull RL , et al.  Gestational diabetes mellitus increases the risk of cardiovascular disease in women with a family history of type 2 diabetes . Diabetes Care. 2006 ; 29 ( 9 ): 2078 - 2083 .

Gunderson EP , Chiang V , Pletcher MJ , et al.  History of gestational diabetes mellitus and future risk of atherosclerosis in mid-life: the Coronary Artery Risk Development in Young Adults study . J Am Heart Assoc. 2014 ; 3 ( 2 ): e000490 .

Retnakaran R . Glucose tolerance status in pregnancy: a window to the future risk of diabetes and cardiovascular disease in young women . Curr Diabetes Rev. 2009 ; 5 ( 4 ): 239 - 244 .

Tobias DK , Hu FB , Forman JP , et al.  Increased risk of hypertension after gestational diabetes mellitus: findings from a large prospective cohort study . Diabetes Care. 2011 ; 34 ( 7 ): 1582 - 1584 .

Tobias DK , Stuart JJ , Li S , et al.  Association of history of gestational diabetes with long-term cardiovascular disease risk in a large prospective cohort of US Women . JAMA Intern Med. 2017 ; 177 ( 12 ): 1735 - 1742 .

Brown HL , Warner JJ , Gianos E , et al.  Promoting risk identification and reduction of cardiovascular disease in women through collaboration with obstetricians and gynecologists: a presidential advisory from the American Heart Association and the American College of Obstetricians and Gynecologists . Circulation. 2018 ; 137 ( 24 ): e843 - ee52 .

Knudsen LS , Christensen IJ , Lottenburger T , et al.  Pre-analytical and biological variability in circulating interleukin 6 in healthy subjects and patients with rheumatoid arthritis . Biomarkers. 2008 ; 13 ( 1 ): 59 - 78 .

Alwan N , Tuffnell DJ , West J . Treatments for gestational diabetes . Cochrane Database Syst Rev. 2009 ; 3 : CD003395 .

Jovanovic-Peterson L , Peterson CM , Reed GF , et al.  Maternal postprandial glucose levels and infant birth weight: the Diabetes in Early Pregnancy Study. The National Institute of Child Health and Human Development—Diabetes in Early Pregnancy Study . Am J Obstet Gynecol. 1991 ; 164 ( 1 Pt 1 ): 103 - 111 .

Bain E , Crane M , Tieu J , et al.  Diet and exercise interventions for preventing gestational diabetes mellitus . Cochrane Database Syst Rev. 0443 ; 2015 ; 4 : CD01 .

Duarte-Gardea MO , Gonzales-Pacheco DM , Reader DM , et al.  Academy of nutrition and dietetics gestational diabetes evidence-based nutrition practice guideline . J Acad Nutr Diet. 2018 ; 118 ( 9 ): 1719 - 1742 .

Rasmussen KM , Yaktine AL , eds.; Institute of Medicine and National Research Council Committee to Reexamine IOM Pregnancy Weight Guidelines . Weight Gain During Pregnancy: Reexamining the Guidelines. National Academies Press ; 2009 .

Jovanovic-Peterson L , Peterson CM . Dietary manipulation as a primary treatment strategy for pregnancies complicated by diabetes . J Am Coll Nutr. 1990 ; 9 ( 4 ): 320 - 325 .

Hernandez TL , Van Pelt RE , Anderson MA , et al.  A higher-complex carbohydrate diet in gestational diabetes mellitus achieves glucose targets and lowers postprandial lipids: a randomized crossover study . Diabetes Care. 2014 ; 37 ( 5 ): 1254 - 1262 .

Hernandez TL , Van Pelt RE , Anderson MA , et al.  Women with gestational diabetes mellitus randomized to a higher-complex carbohydrate/low-fat diet manifest lower adipose tissue insulin resistance, inflammation, glucose, and free fatty acids: a pilot study . Diabetes Care. 2016 ; 39 ( 1 ): 39 - 42 .

Asemi Z , Tabassi Z , Samimi M , et al.  Favourable effects of the Dietary Approaches to Stop Hypertension diet on glucose tolerance and lipid profiles in gestational diabetes: a randomised clinical trial . Br J Nutr. 2013 ; 109 ( 11 ): 2024 - 2030 .

Han S , Middleton P , Shepherd E , et al.  Different types of dietary advice for women with gestational diabetes mellitus . Cochrane Database Syst Rev. 2017 ; 2 : CD009275 .

Yamamoto JM , Kellett JE , Balsells M , et al.  Gestational diabetes mellitus and diet: a systematic review and meta-analysis of randomized controlled trials examining the impact of modified dietary interventions on maternal glucose control and neonatal birth weight . Diabetes Care. 2018 ; 41 ( 7 ): 1346 - 1361 .

Major CA , Henry MJ , De Veciana M , et al.  The effects of carbohydrate restriction in patients with diet-controlled gestational diabetes . Obstet Gynecol. 1998 ; 91 ( 4 ): 600 - 604 .

American College of Obstetrics and Gynecology . Practice Bulletin No. 137: gestational diabetes mellitus . Obstet Gynecol. 2013 ; 122 ( 2 Pt 1 ): 406 - 416 .

Metzger BE , Buchanan TA , Coustan DR , et al.  Summary and recommendations of the Fifth International Workshop-Conference on Gestational Diabetes Mellitus . Diabetes Care. 2007 ; 30 ( suppl 2 ): S251 - S260 .

American Diabetes Association . Standards of medical care in diabetes-2011 . Diabetes Care. 2011 ; 34 ( suppl 1 ): S11 - S61 .

Battaglia FC , Meschia G. An Introduction to Fetal Physiology . Academic Press ; 1986 .

Harding JE , Johnston BM . Nutrition and fetal growth . Reprod Fertil Dev. 1995 ; 7 ( 3 ): 539 - 547 .

Sweeting A , Markovic TP , Mijatovic J , et al.  The carbohydrate threshold in pregnancy and gestational diabetes: how low can we go? Nutrients. 2021 ; 13 ( 8 ): 2599 .

Morisaki N , Nagata C , Yasuo S , et al.  Optimal protein intake during pregnancy for reducing the risk of fetal growth restriction: the Japan Environment and Children’s Study . Br J Nutr. 2018 ; 120 ( 12 ): 1432 - 1440 .

Rizzo T , Metzger BE , Burns WJ , et al.  Correlations between antepartum maternal metabolism and intelligence of offspring . N Engl J Med. 1991 ; 325 ( 13 ): 911 - 916 .

Shaw GM , Yang W . Women’s periconceptional lowered carbohydrate intake and NTD-affected pregnancy risk in the era of prefortification with folic acid . Birth Defects Res. 2019 ; 111 ( 5 ): 248 - 253 .

Mijatovic J , Louie JCY , Buso MEC , et al.  Effects of a modestly lower carbohydrate diet in gestational diabetes: a randomized controlled trial . Am J Clin Nutr. 2020 ; 112 ( 2 ): 284 - 292 .

Shubert PJ , Gordon MC , Landon MB , et al.  Ketoacids attenuate glucose uptake in human trophoblasts isolated from first-trimester chorionic villi . Am J Obstet Gynecol. 1996 ; 175 ( 1 ): 56 - 62 .

Catalano PM , Mele L , Landon MB , et al.  Inadequate weight gain in overweight and obese pregnant women: what is the effect on fetal growth? Am J Obstet Gynecol. 2014 ; 211 ( 2 ): 137 e1 - 137 e7 .

Faucher MA , Barger MK . Gestational weight gain in obese women by class of obesity and select maternal/newborn outcomes: a systematic review . Women Birth. 2015 ; 28 ( 3 ): e70 - e79 .

Viecceli C , Remonti LR , Hirakata VN , et al.  Weight gain adequacy and pregnancy outcomes in gestational diabetes: a meta-analysis . Obes Rev. 2017 ; 18 ( 5 ): 567 - 580 .

Kainer F , Weiss PA , Huttner U , et al.  Levels of amniotic fluid insulin and profiles of maternal blood glucose in pregnant women with diabetes type-I . Early Hum Dev. 1997 ; 49 ( 2 ): 97 - 105 .

de Veciana M , Major CA , Morgan MA , et al.  Postprandial versus preprandial blood glucose monitoring in women with gestational diabetes mellitus requiring insulin therapy . N Engl J Med. 1995 ; 333 ( 19 ): 1237 - 1241 .

Hernandez TL . Glycemic targets in pregnancies affected by diabetes: historical perspective and future directions . Curr Diab Rep. 2015 ; 15 ( 1 ): 565 .

Abell SK , Boyle JA , Earnest A , et al.  Impact of different glycaemic treatment targets on pregnancy outcomes in gestational diabetes . Diabet Med. 2019 ; 36 ( 2 ): 177 - 183 .

Garner P , Okun N , Keely E , et al.  A randomized controlled trial of strict glycemic control and tertiary level obstetric care versus routine obstetric care in the management of gestational diabetes: a pilot study . Am J Obstet Gynecol. 1997 ; 177 ( 1 ): 190 - 195 .

Langer O , Levy J , Brustman L , et al.  Glycemic control in gestational diabetes mellitus--how tight is tight enough: small for gestational age versus large for gestational age? Am J Obstet Gynecol . 1989 ; 161 ( 3 ): 646 - 653 .

Langer O , Rodriguez DA , Xenakis EM , et al.  Intensified versus conventional management of gestational diabetes . Am J Obstet Gynecol. 1994 ; 170 ( 4 ): 1036 - 1046 .

Pregnancy outcomes in the diabetes control and complications trial . Am J Obstet Gynecol. 1996 ; 174 ( 4 ): 1343 - 1353 .

Martis R , Brown J , Alsweiler J , et al.  Different intensities of glycaemic control for women with gestational diabetes mellitus . Cochrane Database Syst Rev. 2016 ; 4 : CD011624 .

Snyder JMI , Melzter S , Nadeau J . Gestational diabetes and glycemic control: a randomized clinical trial . Am J Obstet Gynecol. 1998 ; 178 ( 1 Pt 2 ): S55 .

Pertot T , Molyneaux L , Tan K , et al.  Can common clinical parameters be used to identify patients who will need insulin treatment in gestational diabetes mellitus? Diabetes Care. 2011 ; 34 ( 10 ): 2214 - 2216 .

Rowan JA , Hague WM , Gao W , et al.  Metformin versus insulin for the treatment of gestational diabetes . N Engl J Med. 2008 ; 358 ( 19 ): 2003 - 2015 .

Murphy HR , Rayman G , Duffield K , et al.  Changes in the glycemic profiles of women with type 1 and type 2 diabetes during pregnancy . Diabetes Care. 2007 ; 30 ( 11 ): 2785 - 2791 .

Padmanabhan S , McLean M , Cheung NW . Falling insulin requirements are associated with adverse obstetric outcomes in women with preexisting diabetes . Diabetes Care. 2014 ; 37 ( 10 ): 2685 - 2692 .

Wong VW . Gestational diabetes mellitus in five ethnic groups: a comparison of their clinical characteristics . Diabet Med. 2012 ; 29 ( 3 ): 366 - 371 .

Barnes RA , Wong T , Ross GP , et al.  A novel validated model for the prediction of insulin therapy initiation and adverse perinatal outcomes in women with gestational diabetes mellitus . Diabetologia. 2016 ; 59 ( 11 ): 2331 - 2338 .

Ryu RJ , Hays KE , Hebert MF . Gestational diabetes mellitus management with oral hypoglycemic agents . Semin Perinatol. 2014 ; 38 ( 8 ): 508 - 515 .

Camelo Castillo W , Boggess K , Sturmer T , et al.  Trends in glyburide compared with insulin use for gestational diabetes treatment in the United States, 2000-2011 . Obstet Gynecol. 2014 ; 123 ( 6 ): 1177 - 1184 .

Langer O , Conway DL , Berkus MD , et al.  A comparison of glyburide and insulin in women with gestational diabetes mellitus . N Engl J Med. 2000 ; 343 ( 16 ): 1134 - 1138 .

Kremer CJ , Duff P . Glyburide for the treatment of gestational diabetes . Am J Obstet Gynecol. 2004 ; 190 ( 5 ): 1438 - 1439 .

Camelo Castillo W , Boggess K , Sturmer T , et al.  Association of adverse pregnancy outcomes with glyburide vs insulin in women with gestational diabetes . JAMA Pediatr. 2015 ; 169 ( 5 ): 452 - 458 .

Schwartz RA , Rosenn B , Aleksa K , et al.  Glyburide transport across the human placenta . Obstet Gynecol. 2015 ; 125 ( 3 ): 583 - 588 .

Cheng YW , Chung JH , Block-Kurbisch I , et al.  Treatment of gestational diabetes mellitus: glyburide compared to subcutaneous insulin therapy and associated perinatal outcomes . J Matern Fetal Neonatal Med. 2012 ; 25 ( 4 ): 379 - 384 .

Lindsay RS , Loeken MR . Metformin use in pregnancy: promises and uncertainties . Diabetologia. 2017 ; 60 ( 9 ): 1612 - 1619 .

Charles B , Norris R , Xiao X , et al.  Population pharmacokinetics of metformin in late pregnancy . Ther Drug Monit. 2006 ; 28 ( 1 ): 67 - 72 .

Tarry-Adkins JL , Aiken CE , Ozanne SE . Neonatal, infant, and childhood growth following metformin versus insulin treatment for gestational diabetes: a systematic review and meta-analysis . PLoS Med. 2019 ; 16 ( 8 ): e1002848 .

Feig DS , Donovan LE , Zinman B , et al.  Metformin in women with type 2 diabetes in pregnancy (MiTy): a multicentre, international, randomised, placebo-controlled trial . Lancet Diabetes Endocrinol. 2020 ; 8 ( 10 ): 834 - 844 .

Niromanesh S , Alavi A , Sharbaf FR , et al.  Metformin compared with insulin in the management of gestational diabetes mellitus: a randomized clinical trial . Diabetes Res Clin Pract. 2012 ; 98 ( 3 ): 422 - 429 .

Khin MO , Gates S , Saravanan P . Predictors of metformin failure in gestational diabetes mellitus (GDM) . Diabetes Metab Syndr. 2018 ; 12 ( 3 ): 405 - 410 .

Rowan JA , Rush EC , Obolonkin V , et al.  Metformin in gestational diabetes: the offspring follow-up (MiG TOFU): body composition at 2 years of age . Diabetes Care. 2011 ; 34 ( 10 ): 2279 - 2284 .

Rowan JA , Rush EC , Plank LD , et al.  Metformin in gestational diabetes: the offspring follow-up (MiG TOFU): body composition and metabolic outcomes at 7-9 years of age . BMJ Open Diabetes Res Care. 2018 ; 6 ( 1 ): e000456 .

Hanem LGE , Stridsklev S , Juliusson PB , et al.  Metformin use in PCOS pregnancies increases the risk of offspring overweight at 4 years of age: follow-up of two RCTs . J Clin Endocrinol Metab. 2018 ; 103 ( 4 ): 1612 - 1621 .

Brown J , Martis R , Hughes B , et al.  Oral anti-diabetic pharmacological therapies for the treatment of women with gestational diabetes . Cochrane Database Syst Rev. 2017 ; 1 : CD011967 .

Balsells M , Garcia-Patterson A , Sola I , et al.  Glibenclamide, metformin, and insulin for the treatment of gestational diabetes: a systematic review and meta-analysis . BMJ. 2015 ; 350 : h102 .

Poolsup N , Suksomboon N , Amin M . Efficacy and safety of oral antidiabetic drugs in comparison to insulin in treating gestational diabetes mellitus: a meta-analysis . PLoS One. 2014 ; 9 ( 10 ): e109985 .

Nachum Z , Zafran N , Salim R , et al.  Glyburide versus metformin and their combination for the treatment of gestational diabetes mellitus: a randomized controlled study . Diabetes Care. 2017 ; 40 ( 3 ): 332 - 337 .

Sacco F , Calderone A , Castagnoli L , et al.  The cell-autonomous mechanisms underlying the activity of metformin as an anticancer drug . Br J Cancer. 2016 ; 115 ( 12 ): 1451 - 1456 .

Barbour LA , Davies JK . Comment on Nachum et al . Glyburide versus metformin and their combination for the treatment of gestational diabetes mellitus: a randomized controlled study. Diabetes Care. 2017;40:332-337. Diabetes Care. 2017; 40 (8):e115.

Rao U , de Vries B , Ross GP , et al.  Fetal biometry for guiding the medical management of women with gestational diabetes mellitus for improving maternal and perinatal health . Cochrane Database Syst Rev. 2019 ; 9 : CD012544 .

Schaefer-Graf UM , Kjos SL , Fauzan OH , et al.  A randomized trial evaluating a predominantly fetal growth-based strategy to guide management of gestational diabetes in Caucasian women . Diabetes Care. 2004 ; 27 ( 2 ): 297 - 302 .

Schaefer-Graf UM , Wendt L , Sacks DA , et al.  How many sonograms are needed to reliably predict the absence of fetal overgrowth in gestational diabetes mellitus pregnancies? Diabetes Care. 2011 ; 34 ( 1 ): 39 - 43 .

Kjos SL , Schaefer-Graf UM . Modified therapy for gestational diabetes using high-risk and low-risk fetal abdominal circumference growth to select strict versus relaxed maternal glycemic targets . Diabetes Care. 2007 ; 30 ( suppl 2 ): S200 - S205 .

Nelson L , Wharton B , Grobman WA . Prediction of large for gestational age birth weights in diabetic mothers based on early third-trimester sonography . J Ultrasound Med. 2011 ; 30 ( 12 ): 1625 - 1628 .

McLean A , Katz M , Oats J , et al.  Rethinking third trimester ultrasound measurements and risk of adverse neonatal outcomes in pregnancies complicated by hyperglycaemia: a retrospective study . Aust N Z J Obstet Gynaecol. 2021 ; 61 ( 3 ): 366 - 372 .

American College of Obstetricians and Gynecologists . ACOG Committee Opinion No. 560: medically indicated late-preterm and early-term deliveries . Obstet Gynecol. 2013 ; 121 ( 4 ): 908 - 910 .

Metcalfe A , Hutcheon JA , Sabr Y , et al.  Timing of delivery in women with diabetes: a population-based study . Acta Obstet Gynecol Scand. 2020 ; 99 ( 3 ): 341 - 349 .

Hunt KJ , Logan SL , Conway DL , et al.  Postpartum screening following GDM: how well are we doing? Curr Diab Rep. 2010 ; 10 ( 3 ): 235 - 241 .

Gabbe SG , Landon MB , Warren-Boulton E , et al.  Promoting health after gestational diabetes: a National Diabetes Education Program call to action . Obstet Gynecol. 2012 ; 119 ( 1 ): 171 - 176 .

Ehrlich SF , Hedderson MM , Feng J , et al.  Change in body mass index between pregnancies and the risk of gestational diabetes in a second pregnancy . Obstet Gynecol. 2011 ; 117 ( 6 ): 1323 - 1330 .

Kim C , Newton KM , Knopp RH . Gestational diabetes and the incidence of type 2 diabetes: a systematic review . Diabetes Care. 2002 ; 25 ( 10 ): 1862 - 1868 .

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

Dornhorst A , Bailey PC , Anyaoku V , et al.  Abnormalities of glucose tolerance following gestational diabetes . Q J Med. 1990 ; 77 ( 284 ): 1219 - 1228 .

Aroda VR , Christophi CA , Edelstein SL , et al.  The effect of lifestyle intervention and metformin on preventing or delaying diabetes among women with and without gestational diabetes: the Diabetes Prevention Program outcomes study 10-year follow-up . J Clin Endocrinol Metab. 2015 ; 100 ( 4 ): 1646 - 1653 .

Pirc LK , Owens JA , Crowther CA , et al.  Mild gestational diabetes in pregnancy and the adipoinsular axis in babies born to mothers in the ACHOIS randomised controlled trial . BMC Pediatr. 2007 ; 7 : 18 .

Landon MB , Rice MM , Varner MW , et al.  Mild gestational diabetes mellitus and long-term child health . Diabetes Care. 2015 ; 38 ( 3 ): 445 - 452 .

Dabelea D , Pettitt DJ . Intrauterine diabetic environment confers risks for type 2 diabetes mellitus and obesity in the offspring, in addition to genetic susceptibility . J Pediatr Endocrinol Metab. 2001 ; 14 ( 8 ): 1085 - 1091 .

Pettitt DJ , Baird HR , Aleck KA , et al.  Excessive obesity in offspring of Pima Indian women with diabetes during pregnancy . N Engl J Med. 1983 ; 308 ( 5 ): 242 - 245 .

Gillman MW , Rifas-Shiman S , Berkey CS , Field AE , Colditz GA . Maternal gestational diabetes, birth weight, and adolescent obesity . Pediatrics. 2003 ; 111 ( 3 ): e221 - e226 .

Plagemann A , Harder T , Kohlhoff R , et al.  Overweight and obesity in infants of mothers with long-term insulin-dependent diabetes or gestational diabetes . Int J Obes Relat Metab Disord. 1997 ; 21 ( 6 ): 451 - 456 .

Pribylova H , Dvorakova L . Long-term prognosis of infants of diabetic mothers. Relationship between metabolic disorders in newborns and adult offspring . Acta Diabetol. 1996 ; 33 ( 1 ): 30 - 34 .

Silverman BL , Metzger BE , Cho NH , et al.  Impaired glucose tolerance in adolescent offspring of diabetic mothers: relationship to fetal hyperinsulinism . Diabetes Care. 1995 ; 18 ( 5 ): 611 - 617 .

Crume TL , Ogden L , West NA , et al.  Association of exposure to diabetes in utero with adiposity and fat distribution in a multiethnic population of youth: the Exploring Perinatal Outcomes among Children (EPOCH) Study . Diabetologia. 2011 ; 54 ( 1 ): 87 - 92 .

West NA , Crume TL , Maligie MA , et al.  Cardiovascular risk factors in children exposed to maternal diabetes in utero . Diabetologia. 2011 ; 54 ( 3 ): 504 - 507 .

Hillier TA , Pedula KL , Schmidt MM , et al.  Childhood obesity and metabolic imprinting: the ongoing effects of maternal hyperglycemia . Diabetes Care. 2007 ; 30 ( 9 ): 2287 - 2292 .

Whincup PH , Kaye SJ , Owen CG , et al.  Birth weight and risk of type 2 diabetes: a systematic review . JAMA. 2008 ; 300 ( 24 ): 2886 - 2897 .

Guerrero-Romero F , Aradillas-Garcia C , Simental-Mendia LE , et al.  Birth weight, family history of diabetes, and metabolic syndrome in children and adolescents . J Pediatr. 2010 ; 156 ( 5 ): 719 - 723 , 23 e1.

Harder T , Roepke K , Diller N , et al.  Birth weight, early weight gain, and subsequent risk of type 1 diabetes: systematic review and meta-analysis . Am J Epidemiol. 2009 ; 169 ( 12 ): 1428 - 1436 .

Reece EA . The fetal and maternal consequences of gestational diabetes mellitus . J Matern Fetal Neonatal Med. 2010 ; 23 ( 3 ): 199 - 203 .

Ornoy A . Prenatal origin of obesity and their complications: gestational diabetes, maternal overweight and the paradoxical effects of fetal growth restriction and macrosomia . Reprod Toxicol. 2011 ; 32 ( 2 ): 205 - 212 .

Page KA , Romero A , Buchanan TA , et al.  Gestational diabetes mellitus, maternal obesity, and adiposity in offspring . J Pediatr. 2014 ; 164 ( 4 ): 807 - 810 .

Grunnet LG , Hansen S , Hjort L , et al.  Adiposity, dysmetabolic traits, and earlier onset of female puberty in adolescent offspring of women with gestational diabetes mellitus: a clinical study within the Danish National Birth Cohort . Diabetes Care. 2017 ; 40 ( 12 ): 1746 - 1755 .

Logan KM , Emsley RJ , Jeffries S , et al.  Development of early adiposity in infants of mothers with gestational diabetes mellitus . Diabetes Care. 2016 ; 39 ( 6 ): 1045 - 1051 .

Catalano PM , Farrell K , Thomas A , et al.  Perinatal risk factors for childhood obesity and metabolic dysregulation . Am J Clin Nutr. 2009 ; 90 ( 5 ): 1303 - 1313 .

Chung WK , Erion K , Florez JC , et al.  Precision medicine in diabetes: a consensus report from the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) . Diabetes Care. 2020 ; 43 ( 7 ): 1617 - 1635 .

Cheney C , Shragg P , Hollingsworth D . Demonstration of heterogeneity in gestational diabetes by a 400-kcal breakfast meal tolerance test . Obstet Gynecol. 1985 ; 65 ( 1 ): 17 - 23 .

Powe CE , Allard C , Battista MC , et al.  Heterogeneous contribution of insulin sensitivity and secretion defects to gestational diabetes mellitus . Diabetes Care. 2016 ; 39 ( 6 ): 1052 - 1055 .

Catalano PM , Tyzbir ED , Roman NM , et al.  Longitudinal changes in insulin release and insulin resistance in nonobese pregnant women . Am J Obstet Gynecol. 1991 ; 165 ( 6 Pt 1 ): 1667 - 1672 .

Powe CE , Hivert MF , Udler MS . Defining heterogeneity among women with gestational diabetes mellitus . Diabetes. 2020 ; 69 ( 10 ): 2064 - 2074 .

Benhalima K , Van Crombrugge P , Moyson C , et al.  Characteristics and pregnancy outcomes across gestational diabetes mellitus subtypes based on insulin resistance . Diabetologia. 2019 ; 62 ( 11 ): 2118 - 2128 .

Sweeting A , Park F , Hyett J . The first trimester: prediction and prevention of the great obstetrical syndromes . Best Pract Res Clin Obstet Gynaecol. 2015 ; 29 ( 2 ): 183 - 193 .

Coustan DR , Nelson C , Carpenter MW , et al.  Maternal age and screening for gestational diabetes: a population-based study . Obstet Gynecol. 1989 ; 73 ( 4 ): 557 - 561 .

Lavin JP Jr . Screening of high-risk and general populations for gestational diabetes. Clinical application and cost analysis . Diabetes. 1985 ; 34 ( suppl 2 ): 24 - 27 .

Weeks JW , Major CA , de Veciana M , et al.  Gestational diabetes: does the presence of risk factors influence perinatal outcome? Am J Obstet Gynecol. 1994 ; 171 ( 4 ): 1003 - 1007 .

Cosson E , Benbara A , Pharisien I , et al.  Diagnostic and prognostic performances over 9 years of a selective screening strategy for gestational diabetes mellitus in a cohort of 18,775 subjects . Diabetes Care. 2013 ; 36 ( 3 ): 598 - 603 .

Chevalier N , Fenichel P , Giaume V , et al.  Universal two-step screening strategy for gestational diabetes has weak relevance in French Mediterranean women: should we simplify the screening strategy for gestational diabetes in France? Diabetes Metab. 2011 ; 37 ( 5 ): 419 - 425 .

Moses RG , Moses J , Davis WS . Gestational diabetes: do lean young Caucasian women need to be tested? Diabetes Care. 1998 ; 21 ( 11 ): 1803 - 1806 .

Avalos GE , Owens LA , Dunne F , et al.  Applying current screening tools for gestational diabetes mellitus to a European population: is it time for change? Diabetes Care. 2013 ; 36 ( 10 ): 3040 - 3044 .

Teede HJ , Harrison CL , Teh WT , et al.  Gestational diabetes: development of an early risk prediction tool to facilitate opportunities for prevention . Aust N Z J Obstet Gynaecol. 2011 ; 51 ( 6 ): 499 - 504 .

Syngelaki A , Pastides A , Kotecha R , et al.  First-trimester screening for gestational diabetes mellitus based on maternal characteristics and history . Fetal Diagn Ther. 2015 ; 38 ( 1 ): 14 - 21 .

Savvidou M , Nelson SM , Makgoba M , et al.  First-trimester prediction of gestational diabetes mellitus: examining the potential of combining maternal characteristics and laboratory measures . Diabetes. 2010 ; 59 ( 12 ): 3017 - 3022 .

Lamain-de Ruiter M , Kwee A , Naaktgeboren CA , et al.  Prediction models for the risk of gestational diabetes: a systematic review . Diagn Progn Res. 2017 ; 1 : 3 .

Sweeting AN , Wong J , Appelblom H , et al.  A novel early pregnancy risk prediction model for gestational diabetes mellitus . Fetal Diagn Ther. 2019 ; 45 ( 2 ): 76 - 84 .

Sweeting AN , Wong J , Appelblom H , et al.  A first trimester prediction model for gestational diabetes utilizing aneuploidy and pre-eclampsia screening markers . J Matern Fetal Neonatal Med. 2018 ; 31 ( 16 ): 2122 - 2130 .

Aronson JK , Ferner RE . Biomarkers-a general review . Curr Protoc Pharmacol. 2017 ; 76 : 9 23 19 17 .

Allinson JL . Clinical biomarker validation . Bioanalysis. 2018 ; 10 ( 12 ): 957 - 968 .

Sattar N , Wannamethee SG , Forouhi NG . Novel biochemical risk factors for type 2 diabetes: pathogenic insights or prediction possibilities? Diabetologia. 2008 ; 51 ( 6 ): 926 - 940 .

O’Malley EG , Reynolds CME , Killalea A , et al.  The use of biomarkers at the end of the second trimester to predict Gestational Diabetes Mellitus . Eur J Obstet Gynecol Reprod Biol. 2020 ; 250 : 101 - 106 .

Richardson AC , Carpenter MW . Inflammatory mediators in gestational diabetes mellitus . Obstet Gynecol Clin North Am. 2007 ; 34 ( 2 ): 213 - 224 , viii.

Eleftheriades M , Papastefanou I , Lambrinoudaki I , et al.  Elevated placental growth factor concentrations at 11-14 weeks of gestation to predict gestational diabetes mellitus . Metabolism. 2014 ; 63 ( 11 ): 1419 - 1425 .

Lovati E , Beneventi F , Simonetta M , et al.  Gestational diabetes mellitus: including serum pregnancy-associated plasma protein-A testing in the clinical management of primiparous women? A case-control study . Diabetes Res Clin Pract. 2013 ; 100 ( 3 ): 340 - 347 .

White SL , Lawlor DA , Briley AL , et al.  Early antenatal prediction of gestational diabetes in obese women: development of prediction tools for targeted intervention . PLoS One. 2016 ; 11 ( 12 ): e0167846 .

Rasanen JP , Snyder CK , Rao PV , et al.  Glycosylated fibronectin as a first-trimester biomarker for prediction of gestational diabetes . Obstet Gynecol. 2013 ; 122 ( 3 ): 586 - 594 .

Watanabe N , Morimoto S , Fujiwara T , et al.  Prediction of gestational diabetes mellitus by soluble (pro)renin receptor during the first trimester . J Clin Endocrinol Metab. 2013 ; 98 ( 6 ): 2528 - 2535 .

Theriault S , Giguere Y , Masse J , et al.  Early prediction of gestational diabetes: a practical model combining clinical and biochemical markers . Clin Chem Lab Med. 2016 ; 54 ( 3 ): 509 - 518 .

Syngelaki A , Kotecha R , Pastides A , et al.  First-trimester biochemical markers of placentation in screening for gestational diabetes mellitus . Metabolism. 2015 ; 64 ( 11 ): 1485 - 1489 .

Gobl CS , Bozkurt L , Rivic P , et al.  A two-step screening algorithm including fasting plasma glucose measurement and a risk estimation model is an accurate strategy for detecting gestational diabetes mellitus . Diabetologia. 2012 ; 55 ( 12 ): 3173 - 3181 .

Australasian Diabetes in Pregnancy Society. Diagnostic testing for gestational diabetes mellitus (GDM) during the COVID 19 pandemic: antenatal and postnatal testing advice: a statement from the Australasian Diabetes in Pregnancy Society (ADIPS) . Last updated April 7, 2020. Accessed June 12, 2021 . https://www.adips.org/documents/COVID19-WITHQLDGUIDELINES0704201150ADIPSADSADEADAupdated.pdf

Royal College of Obstetricians . Guidance for maternal medicine in the evolving coronavirus Covid-19 pandemic . Published July 10, 2020 . Accessed June 30, 2021. https://www.rcog.org.uk/globalassets/documents/guidelines/2020-07-10-guidance-for-maternal-medicine.pdf

Simmons D , Rudland VL , Wong V , et al.  Options for screening for gestational diabetes mellitus during the SARS-CoV-2 pandemic . Aust N Z J Obstet Gynaecol. 2020 ; 60 ( 5 ): 660 - 666 .

McIntyre HD , Gibbons KS , Ma RCW , et al.  Testing for gestational diabetes during the COVID-19 pandemic. An evaluation of proposed protocols for the United Kingdom, Canada and Australia . Diabetes Res Clin Pract. 2020 ; 167 : 108353 .

Meek CL , Lindsay RS , Scott EM , et al.  Approaches to screening for hyperglycaemia in pregnant women during and after the COVID-19 pandemic . Diabet Med. 2021 ; 38 ( 1 ): e14380 .

Email alerts

Citing articles via.

• About the Endocrine Society
• Recommend to Your Librarian
• Advertising and Corporate Services
• Journals Career Network

Affiliations

• Online ISSN 1945-7189
• Print ISSN 0163-769X
• Copyright © 2024 Endocrine Society
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• PubMed/Medline
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Gestational diabetes mellitus—recent literature review.

1. Introduction

2. aim of the study, 3. material and methods, 4. results and discussion, 4.1. epidemiology, 4.2. gdm risk factors, 4.3. diagnosing gdm, 4.4. pathogenesis of carbohydrate metabolism disorders in pregnancy, 4.4.1. insulin resistance, 4.4.2. β-cell dysfunction, 4.4.3. other factors, 4.5. covid-19 pandemic and gdm, 4.6. treatment of gestational diabetes, 4.6.1. nutritional treatment, 4.6.2. exercise in gdm, 4.6.3. pharmacological treatment, 5. conclusions, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

• Buchanan, T.A.; Xiang, A.H.; Page, K.A. Gestational Diabetes Mellitus: Risks and Management during and after Pregnancy. Nat. Rev. Endocrinol. 2012 , 8 , 639–649. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Crowther, C.A.; Hiller, J.E.; Moss, J.R.; McPhee, A.J.; Jeffries, W.S.; Robinson, J.S.; Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) Trial Group. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. N. Engl. J. Med. 2005 , 352 , 2477–2486. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, H.; Li, N.; Chivese, T.; Werfalli, M.; Sun, H.; Yuen, L.; Hoegfeldt, C.A.; Elise Powe, C.; Immanuel, J.; Karuranga, S.; et al. IDF Diabetes Atlas: Estimation of Global and Regional Gestational Diabetes Mellitus Prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group’s Criteria. Diabetes Res. Clin. Pract. 2022 , 183 , 109050. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kondracki, A.J.; Valente, M.J.; Ibrahimou, B.; Bursac, Z. Risk of large for gestational age births at early, full and late term in relation to pre-pregnancy body mass index: Mediation by gestational diabetes status. Paediatr. Perinat. Epidemiol. 2022 , 36 , 566–576. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lee, K.W.; Ching, S.M.; Ramachandran, V.; Yee, A.; Hoo, F.K.; Chia, Y.C.; Sulaiman, W.A.W.; Suppiah, S.; Mohamed, M.H.; Veettil, S.K. Prevalence and risk factors of gestational diabetes mellitus in Asia: A systematic review and meta-analysis. BMC Pregnancy Childbirth 2018 , 18 , 494. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• McIntyre, H.D.; Catalano, P.; Zhang, C.; Desoye, G.; Mathiesen, E.R.; Damm, P. Gestational diabetes mellitus. Nat. Rev. Dis. Primers 2019 , 5 , 47. [ Google Scholar ] [ CrossRef ]
• Lenoir-Wijnkoop, I.; Van Der Beek, E.M.; Garssen, J.; Nuijten, M.J.C.; Uauy, R.D. Health economic modeling to assess short-term costs of maternal overweight, gestational diabetes, and related macrosomia—A pilot evaluation. Front. Pharmacol. 2015 , 6 , 103. [ Google Scholar ] [ CrossRef ]
• Xu, T.; Dainelli, L.; Yu, K.; Ma, L.; Zolezzi, I.S.; Detzel, P.; Fang, H. The short-term health and economic burden of gestational diabetes mellitus in China: A modelling study. BMJ Open 2017 , 7 , e018893. [ Google Scholar ] [ CrossRef ]
• Plows, J.F.; Stanley, J.L.; Baker, P.N.; Reynolds, C.M.; Vickers, M.H. The Pathophysiology of Gestational Diabetes Mellitus. Int. J. Mol. Sci. 2018 , 19 , 3342. [ Google Scholar ] [ CrossRef ]
• Oliveira, M.M.; Andrade, K.F.O.; Lima, G.H.S.; Rocha, T.C. Metformin versus glyburide in treatment and control of gestational diabetes mellitus: A systematic review with meta-analysis. Einstein 2022 , 20 , eRW6155. [ Google Scholar ] [ CrossRef ]
• Chen, C.; Xu, X.; Yan, Y. Estimated global overweight and obesity burden in pregnant women based on panel data model. PLoS ONE 2018 , 13 , e0202183. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bucley, B.S.; Harreiter, J.; Damm, P.; Corcoy, R.; Chico, A.; Simmons, D.; Vellinga, A.; Dunne, F. Gestational diabetes mellitus in Europe: Prevalence, current screening practice and barriers to screening. Diabet. Med. 2012 , 29 , 844–854. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Moses, R.; Griffits, R.; Daviess, W. Gestational diabetes: Do all women need to be tested? Aust. N. Z. J. Obstet. Gynaecol. 1995 , 35 , 387–389. [ Google Scholar ] [ CrossRef ]
• Lao, T.T.; Ho, L.-F.; Chan, B.C.P.; Leung, W.-C. Maternal Age and Prevalence of Gestational Diabetes Mellitus. Diabetes Care 2006 , 29 , 948–949. [ Google Scholar ] [ CrossRef ]
• Miller, C.; Lim, E. The risk of diabetes after giving birth to a macrosomic infant: Data from the NHANES cohort. Matern. Health Neonatol. Perinatol. 2021 , 7 , 12. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Schwartz, N.; Nachum, Z.; Green, S.M. The prevalence of gestational diabetes mellitus recurrence—Effect of ethnicity and parity: A metaanalysis. Am. J. Obstet. Gynecol. 2015 , 213 , 310–317. [ Google Scholar ] [ CrossRef ]
• Kim, C.; Berger, K.D.; Chamany, S. Recurrence of Gestational Diabetes Mellitus A systematic review. Diabetes Care 2007 , 30 , 1314–1319. [ Google Scholar ] [ CrossRef ]
• Torloni, M.R.; Betran, A.P.; Horta, B.L.; Nakamura, M.U.; Atallah, N.A.; Maron, A.F.; Valente, O. Prepregnancy BMI and the risk of gestational diabetes: A systemic review of literature with meta-analysis. Obes. Rev. 2009 , 10 , 194–203. [ Google Scholar ] [ CrossRef ]
• Song, X.; Chen, L.; Zhang, S.; Liu, Y.; Wei, J.; Wang, T.; Qin, J. Gestational Diabetes Mellitus and High Triglyceride Levels Mediate the Association between Pre-Pregnancy Overweight/Obesity and Macrosomia: A Prospective Cohort Study in Central China. Nutrients 2022 , 14 , 3347. [ Google Scholar ] [ CrossRef ]
• Mikola, M.; Hillesmaa, V.; Halttunen, M.; Suhonen, L.; Tiitinen, A. Obstetric outcome in women with polycystic ovarian syndrome. Hum. Reprod. 2001 , 16 , 226–229. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Katsarou, A.; Claesson, R.; Shaat, N.; Ignell, V.; Berntorp, V. Seasonal pattern in the diagnosis of gestational diabetes mellitus in southern Sweden. J. Diabetes Res. 2016 , 2016 , 8905474. [ Google Scholar ] [ CrossRef ]
• Wang, P.; Wu, C.S.; Li, C.Y.; Yang, C.P.; Lu, M.C. Seasonality of gestational diabetes mellitus and maternal blood glucose levels: Evidence from Taiwan. Medicine 2020 , 99 , e22684. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chiefari, E.; Pastore, I.; Puccio, L.; Caroleo, P.; Oliverio, R.; Vero, A.; Foti, D.P.; Vero, R.; Brunetti, A. Impact of seasonality on gestational diabetes mellitus. Endocr. Metab. Immune Disord.-Drug Targets 2017 , 17 , 246–252. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bianchi, C.; Pelle, C.D.; Gennaro, G.D.; Aragona, M.; Cela, V.; Delprato, S.; Bertolotto, A. 1392-P: Assisted Reproduction Technology Treatment and Risk of Gestational Diabetes. Diabetes 2020 , 69 (Suppl. 1), 1392-P. [ Google Scholar ] [ CrossRef ]
• Benhalima, K.; Hanssens, M.; Devlieger, R.; Verhaeghe, J.; Mathieu, C. Analysis of pregnancy outcomes using the new IADPSG recommendation compared with the Carpenter and Coustan criteria in an area with a low prevalence of gestational diabetes. Int. J. Endocrinol. 2013 , 2013 , 248121. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• HAPO Study Cooperative Research Group. Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study: Association with maternal body mass index. BJOG 2010 , 117 , 575–584. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Werner, E.F.; Pettfer, C.M.; Zuckerwise, L.; Reel, M.; Funai, E.F.; Henderson, J.; Thung, S.F. Screening for gestational diabetes mellitus: Are the criteria proposed by the International Association of the Diabetes and Pregnancy Study Groups cost-effective? Diabetes Care 2012 , 35 , 529–535. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Williams, C.B.; Iqbal, S.; Zawacki, C.M.; Yu, D.; Brown, M.B.; Herman, W.H. Effect of selective screening for gestational diabetes. Diabetes Care 1999 , 22 , 418–421. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Landon, M.B.; Spong, C.Y.; Thom, E.; Carpenter, M.W.; Ramin, S.M.; Casey, B.; Wapner, R.J.; Varner, M.W.; Rouse, D.J.; Thorp, J.M., Jr.; et al. A multicenter, randomized trial of treatment for mild gestational diabetes. N. Engl. J. Med. 2009 , 361 , 1339–1348. [ Google Scholar ] [ CrossRef ]
• Horvath, K.; Koch, K.; Jeitler, K.; Matyas, E.; Bender, R.; Bastian, H.; Lange, S.; Siebenhofer, A. Effects of treatment in women with gestational diabetes mellitus: Systemic review and meta-analysis. BMJ 2010 , 340 , c1395. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Aubry, E.M.; Raio, L.; Oelhafen, S. Effect of the IADPSG screening strategy for gestational diabetes on perinatal outcomes in Switzerland. Diabetes Res. Clin. Pract. 2021 , 175 , 108830. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hillier, T.A.; Pedula, K.L.; Ogasawara, K.K.; Vesco, K.K.; Oshiro, C.E.S.; Lubarsky, S.L.; Van Marter, J. A Pragmatic, Randomized Clinical Trial of Gestational Diabetes Screening. N. Engl. J. Med. 2021 , 384 , 895–904. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Crowther, C.A.; Samuel, D.; McCowan, L.M.; Edlin, R.; Tran, T.; McKinlay, C.J. Lower versus Higher Glycemic Criteria for Diagnosis of Gestational Diabetes. N. Engl. J. Med. 2022 , 387 , 587–598. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lorenzo-Almoros, A.; Hang, T.; Peiro, C.; Soriano-Guillen, L.; Egido, J.; Tunon, J.; Lorenzo, O. Predictive and diagnostic biomarkers for gestational diabetes and its associated metabolic and cardiovascular diseases. Cardiovasc. Diabetol. 2019 , 18 , 140. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Corcoran, M.S.; Achamallah, N.; O’Loughlin, J.; Stafford, P.; Dicker, P.; Malone, D.F.; Breathnach, F. First trimester serum biomarkers to predict gestational diabetes in a high-risk cohort: Striving for clinically useful thresholds. Eur. J. Obstet. Gynecol. Reprod. Biol. 2018 , 222 , 7–12. [ Google Scholar ] [ CrossRef ]
• Kotzaeridi, G.; Blätter, J.; Eppel, D.; Rosicky, I.; Mittlböck, M.; Yerlikaya-Schatten, G.; Schatten, C.; Husslein, P.; Eppel, W.; Huhn, E.A.; et al. Performance of early risk assessment tools to predict the later development of gestational diabetes. Eur. J. Clin. Investig. 2021 , 51 , e13630. [ Google Scholar ] [ CrossRef ]
• Yong, H.Y.; Shariff, Z.M.; Yusof, B.N.M.; Rejali, Z.; Tee, Y.Y.S.; Bindels, J.; Van Der Beek, E.M. Independent and combined effects of age, body mass index and gestational weight gain on the risk of gestational diabetes mellitus. Sci. Rep. 2020 , 10 , 8486. [ Google Scholar ] [ CrossRef ]
• Homko, C.; Sivan, E.; Chen, X.; Reece, E.A.; Boden, G. Insulin secretion during and after pregnancy in patients with gestational diabetes mellitus. J. Clin. Endocrinol. Metab. 2001 , 86 , 568–573. [ Google Scholar ] [ CrossRef ]
• Baeyens, L.; Hindi, S.; Sorenson, R.L.; German, M.S. β-cell adaptation in pregnancy. Diabetes Obes. Metab. 2016 , 18 (Suppl. 1), 63–70. [ Google Scholar ] [ CrossRef ]
• Baz, B.; Riverline, J.P.; Gautier, J.F. Endocrinology of pregnancy: Gestational Diabetes Mellitus: Definition, aetiological and clinical aspects. Eur. J. Endocrinol. 2016 , 174 , R43–R51. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Barberoglu, Z. Pathophysiology of gestational diabetes mellitus. EMJ Diabetes 2019 , 7 , 97–106. [ Google Scholar ]
• Buchanan, T.A.; Kijos, S.L.; Xiang, A.; Watanabe, R. What is Gestational Diabetes? Diabetes Care 2007 , 30 (Suppl. 2), 105–111. [ Google Scholar ] [ CrossRef ]
• Barbour, L.A.; McCurdy CEHernandez, T.L.; Kirwan, J.P.; Catalano, P.M.; Friedman, J.E. Cellular mechanizm of insulin resistance in normal pregnancy and Gestational Diabetes. Diabetes Care 2007 , 30 (Suppl. 2), 112–119. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Vrachins, N.; Belitsos, P.; Sifakis, S.; Dafopoulos, K.; Siristatidis, C.; Pappa, K.I.; Iliodromiti, Z. Role of adipokines and other inflammatory mediators in gestational diabetes mellitus and previous gestational diabetes mellitus. Int. J. Endocrinol. 2012 , 2012 , 549748. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kirwan, J.P.; Hauguel-De Mouzon, S.; Lepercq, J.; Challier, J.-C.; Huston-Presley, L.; Friedman, J.E.; Kalhan, S.C.; Catalano, P.M. TNF-α is a predictor of insulin resistance in human pregnancy. Diabetes 2002 , 51 , 2207–2213. [ Google Scholar ] [ CrossRef ]
• Qiu, C.; Williams, M.A.; Vadachkoria, S.; Frederick, I.O.; Luthy, D.A. Increased maternal plasma leptin in early pregnancy and risk of gestational diabetes mellitus. Obstet. Gynecol. 2004 , 103 , 519–525. [ Google Scholar ] [ CrossRef ]
• Honnorat, D.; Disse, E.; Millot, L.; Mathiotte, E.; Claret, M.; Charrié, A.; Drai, J.; Garnier, L.; Maurice, C.; Durand, E.; et al. Are third-trimester adipokines associated with higher metabolic risk among women with gestational diabetes? Diabetes Metab. 2015 , 41 , 393–400. [ Google Scholar ] [ CrossRef ]
• Maple-Brown, L.; Ye, C.; Hanley, A.J.; Connelly, P.W.; Sermer, M.; Zinman, B.; Retnakaran, R. Maternal pregravid weight is the primary determinant of serum leptin and its metabolic associations in pregnancy, irrespective of gestational glucose tolerance status. J. Clin. Endocrinol. Metab. 2012 , 97 , 4148–4155. [ Google Scholar ] [ CrossRef ]
• Miehle, K.; Stepan, H.; Fasshauer, M. Leptin, adiponectin and other adipokines in gestational diabetes and pre-eclampsia. Clin. Endocrinol. 2012 , 76 , 2–11. [ Google Scholar ] [ CrossRef ]
• Valencia-Ortega, J.; González-Reynoso, R.; Ramos-Martínez, E.G.; Ferreira-Hermosillo, A.; Peña-Cano, M.I.; Morales-Ávila, E.; Saucedo, R. New Insights into Adipokines in Gestational Diabetes Mellitus. Int. J. Mol. Sci. 2022 , 23 , 6279. [ Google Scholar ] [ CrossRef ]
• Kumar, A.d.L.A.; Corcoy, R. Autoimmunity in Gestational Diabetes A Decade after the HAPO Study. Front. Diabetes 2020 , 28 , 234–242. [ Google Scholar ] [ CrossRef ]
• Gjesing, A.; Rui, G.; Launeborg, J.; Have, C.H.; Hollensted, M.; Andersson, E.; Grarup, N.; Sun, J.; Quan, S.; Brandslund, I. High Prevalence of Diabetes-Predisposing Variants in MODY Genes Among Danish Women With Gestational Diabetes Mellitus. J. Endocr. Soc. 2017 , 1 , 681–690. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mao, H.; Li, Q.; Gao, S. Meta-analysis of the relationship between common type 2 diabetes risk gene variants with gestational diabetes mellitus. PLoS ONE 2012 , 7 , e45882-6. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Lowe, W.L.; Scholtens, D.M.; Sandler, V.; Hayes, M.G. Genetics of gestational diabetes mellitus and maternal metabolism. Curr. Diabetes Rep. 2016 , 16 , 15–24. [ Google Scholar ] [ CrossRef ]
• Barabash, A.; Valerio, J.D.; de la Torre, N.G.; Jimenez, I.; del Valle, L.; Melero, V.; Assaf-Balut, C.; Fuentes, M.; Bordiu, E.; Durán, A.; et al. TCF7L2 rs7903146 polymorphism modulates the association between adherence to a Mediterranean diet and the risk of gestational diabetes mellitus. Metab. Open 2020 , 8 , 100069. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ott, R.; Melchior, K.; Stupin, J.H.; Ziska, T.; Schellong, K.; Henrich, W.; Rancourt, R.C.; Plagemann, A. Reduced insulin receptor expression and altered DNA methylation in fat tissue and blood of women with GDM and offspring. J. Clin. Endocrinol. Metab. 2019 , 104 , 137–149. [ Google Scholar ] [ CrossRef ]
• Reichetzeder, C.; Dwi Putra, S.E.; Pfab, T.; Slovinski, T.; Neuber, C.; Kleuser, B.; Hocher, B. Increased global placental DNA methylation levels are associated with gestational diabetes. Clin. Epigenet. 2016 , 8 , 82. [ Google Scholar ] [ CrossRef ]
• Zhang, Y.; Chen, Y.; Qu, H.; Wang, Y. Methylation of HIF3A promoter CpG islands contributes to insulin resistance in gestational diabetes mellitus. Mol. Genet. Genom. Med. 2019 , 7 , e00583. [ Google Scholar ] [ CrossRef ]
• Assi, E.; D’Addio, F.; Mandò, C.; Maestroni, A.; Loretelli, C.; Ben Nasr, M.; Usuelli, V.; Abdelsalam, A.; Seelam, A.J.; Pastore, I.; et al. Placental proteome abnormalities in women with gestational diabetes and large-for-gestational-age newborns. BMJ Open Diabetes Res. Care 2020 , 8 , e001586. [ Google Scholar ] [ CrossRef ]
• Khosrowbeygi, A.; Rezvanfar, M.R.; Ahmadvand, H. Tumor necrosis factor-α, adiponectin and their ratio in gestational diabetes mellitus. Casp. J. Intern. Med. 2018 , 9 , 71–79. [ Google Scholar ] [ CrossRef ]
• Allotey, J.; Stallings, E.; Bonet, M. Clinical manifestations, risk factors, and maternal and perinatal outcomes of coronavirus disease 2019 in pregnancy: Living systematic review and meta-analysis. BMJ 2020 , 370 , 1–18. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Nouhjah, S.; Jahanfar, S.; Shahbazian, H. Temporary changes in clinical guidelines of gestational diabetes screening and management during COVID-19 outbreak: A narrative review. Diabetes Metab. Syndr. 2020 , 14 , 939–942. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Van Gemert, E.T.; Moses, G.R.; Pape, V.A.; Morris, J.G. Gestational diabetes mellitus testing in the Covid19 pandemic: The problems with simplifying the diagnostic process. Aust. N. Z. J. Obstet. Gynaecol. 2020 , 60 , 671–674. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Simmons, D.; Rudland, L.V.; Wong, V.; Flack, J.; Mackie, A.; Ross, P.G.; Coat, S.; Dalal, R.; Hague, M.B.; Cheung, W.N. Options for screening for gestational diabetes mellitus during the SARS-CoV-2 pandemic. Aust. N. Z. J. Obstet. Gynaecol. 2020 , 60 , 660–666. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• McIntyre, H.D.; Gibbons, S.K.; Ma, C.W.R.; Tam, H.W.; Sacks, A.D.; Lowe, J.; Madsen, R.L.; Catalano, M.P. Testing for gestational diabetes during the COVID-19 pandemic. An evaluation of proposed protocols for the United Kingdom, Canada and Australia. Diabetes Res. Clin. Pract. 2020 , 167 , 108353. [ Google Scholar ] [ CrossRef ]
• McIntyre, H.D.; Moses, R.G. The diagnosis and management of gestational diabetes mellitus in the context of the COVID-19 pandemic. Diabetes Care 2020 , 43 , 1433–1434. [ Google Scholar ] [ CrossRef ]
• Zanardo, V.; Tortora, D.; Sandri, A.; Severino, L.; Mesirca, P.; Straface, G. COVID-19 pandemic: Impact on gestational diabetes mellitus prevalence. Diabetes Res. Clin. Pract. 2022 , 183 , 109149. [ Google Scholar ] [ CrossRef ]
• Hillyard, M.; Sinclair, M.; Murphy, M.; Casson, K.; Mulligan, C. The impact of COVID-19 on the physical activity and sedentary behaviour levels of pregnant women with gestational diabetes. PLoS ONE 2021 , 16 , e0254364. [ Google Scholar ] [ CrossRef ]
• Eberle, C.; Stichling, S. Impact of COVID-19 lockdown on glycemic control in patients with type 1 and type 2 diabetes mellitus: A systematic review. Diabetol. Metab. Syndr. 2021 , 13 , 95. [ Google Scholar ] [ CrossRef ]
• Ghesquière, L.; Garabedian, C.; Drumez, E.; Lemaître, M.; Cazaubiel, M.; Bengler, C.; Vambergue, A. Effects of COVID-19 pandemic lockdown on gestational diabetes mellitus: A retrospective study. Diabetes Metab. 2021 , 47 , 101201. [ Google Scholar ] [ CrossRef ]
• Martis, R.; Brown, J.; Alsweiler, J.; Crawford, T.J.; Crowther, C.T. Different intensities of glycaemic control for women with gestational diabetes mellitus. Cochrane Database Syst. Rev. 2016 , 4 , CD011624. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mitanchez, D.; Ciangura, C.; Jacqueminet, S. How can maternal lifestyle interventions modify the effects of gestational diabetes in the neonate and the offspring? A systematic review of meta-analyses. Nutrients 2020 , 12 , 353. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• American Diabetes Association. 14. Management of Diabetes in Pregnancy: Standards of Medical Care in Diabetes—2020. Diabetes Care 2019 , 43 , S183–S192. [ Google Scholar ]
• Chao, H.; Chen, G.; Wen, X.; Liu, J.; Zhang, J. Dietary control plus nutrition guidance for blood glucose and pregnancy outcomes in women with gestational diabetes. Int. J. Clin. Exp. Med. 2019 , 12 , 2773–2778. [ Google Scholar ]
• Morris, M.A.; Hutchinson, J.; Gianfrancesco, C.; Alwan, N.A.; Carter, M.C.; Scott, E.M.; Cade, J.E. Relationship of the frequency, distribution, and content of meals/snacks to glycaemic control in gestational diabetes: The myfood24 GDM pilot study. Nutrients 2020 , 12 , 3. [ Google Scholar ] [ CrossRef ]
• Hay, W.W. Placental-Fetal Glucose Exchange and Fetal Glucose Metabolism. Trans. Am. Clin. Clim. Assoc. 2006 , 117 , 321–340. [ Google Scholar ]
• Nordic Nutrition of Ministers. Nordic Nutrition Recommendations 2012 , 5th ed.; Norden: Copenhagen, Denmark, 2014; pp. 1–629. [ Google Scholar ]
• Yaktine, A.L.; Rasmussen, K.M.; Youth, F.; National Research Council; Institute of Medicine; Board on Children; Committee to Reexamine IOM Pregnancy Weight Guidelines. Weight Gain During Pregnancy: Reexamining the Guidelines (2009) ; Rasmussen, K.M., Yaktine, A.L., Eds.; The National Academies Press: Washington, DC, USA, 2009. [ Google Scholar ]
• Jamilian, M.; Asemi, Z. The Effect of Soy Intake on Metabolic Profiles of Women with Gestational Diabetes Mellitus. J. Clin. Endocrinol. Metab. 2015 , 100 , 4654–4661. [ Google Scholar ] [ CrossRef ]
• Rasmussen, L.; Poulsen, C.W.; Kampmann, U.; Smedegaard, S.B.; Ovesen, P.G.; Fuglsang, J. Diet and Healthy Lifestyle in the Management of Gestational Diabetes Mellitus. Nutrients 2020 , 12 , 3050. [ Google Scholar ] [ CrossRef ]
• Hernandez, T.L.; Van Pelt, R.E.; Anderson, M.A.; Daniels, L.J.; West, N.A.; Donahoo, W.T.; Friedman, J.E.; Barbour, L.A. A Higher-Complex Carbohydrate Diet in Gestational Diabetes Mellitus Achieves Glucose Targets and Lowers Postprandial Lipids: A Randomized Crossover Study. Diabetes Care 2014 , 37 , 1254–1262. [ Google Scholar ] [ CrossRef ]
• Danielewicz, H.; Myszczyszyn, G.; Debinska, A.; Myszkal, A.; Boznanaski, A.; Hirnle, L. Diet in pregnancy—More than food. Eur. J. Pediatr. 2017 , 176 , 1573–1579. [ Google Scholar ] [ CrossRef ]
• Elshani, B.; Kotori, V.; Daci, A. Role of omega-3 polyunsaturated fatty acids in gestational diabetes, maternal and fetal insights: Current use and future directions. J. Matern.-Fetal Neonatal Med. 2019 , 34 , 124–136. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Kiel, D.; Dodson, E.; Artal, R.; Boehmer, T.; Leet, T. Gestational Weight Gain and Pregnancy Outcomes in Obese Women How Much Is Enough? Obstet. Gynecol. 2007 , 110 , 752–758. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sun, Y.; Shen, Z.; Zhan, Y. Effects of pre-pregnanacy body mass index and gestational weight gain on maternal and infant complications. BMC Pregnancy Childbirth 2020 , 20 , 390. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Bouter, K.E.; Van Raalte, D.H.; Groen, A.K.; Nieuwdorp, M. Role of the Gut Microbiome in the Pathogenesis of Obesity and Obesity-Related Metabolic Dysfunction. Gastroenterology 2017 , 152 , 1671–1678. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Crusell, M.K.W.; Hansen, T.H.; Nielsen, T.S.; Allin, K.H.; Rühlemann, M.C.; Damm, P.; Vestergaard, H.; Rørbye, C.; Jørgensen, N.R.; Christiansen, O.B.; et al. Gestational diabetes is associated with change in the gut microbiota composition in third trimester of pregnancy and postpartum. Microbiome 2018 , 6 , 89. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pellonperä, O.; Mokkala, K.; Houttu, N.; Vahlberg, T.; Koivuniemi, E.; Tertti, K.; Rönnemaa, T.; Laitinen, K. Ecacy of Fish Oil and/or Probiotic Intervention on the Incidence of Gestational Diabetes Mellitus in an At-Risk Group of Overweight and ObeseWomen: A Randomized, Placebo-Controlled, Double-Blind Clinical Trial. Diabetes Care 2019 , 42 , 1009–1017. [ Google Scholar ] [ CrossRef ]
• Callaway, L.K.; McIntyre, H.D.; Barrett, H.L.; Foxcroft, K.; Tremellen, A.; Lingwood, B.E.; Tobin, J.M.; Wilkinson, S.A.; Kothari, A.; Morrison, M.; et al. Probiotics for the Prevention of Gestational Diabetes Mellitus in Overweight and ObeseWomen: Findings From the SPRING Double-blind Randomized Controlled Trial. Diabetes Care 2019 , 42 , dc182248. [ Google Scholar ] [ CrossRef ]
• Pan, J.; Pan, Q.; Chen, Y.; Zhang, H.; Zheng, X. Ecacy of probiotic supplement for gestational diabetes mellitus: A systematic review and meta-analysis. J. Matern.-Fetal Neonatal Med. 2017 , 32 , 317–323. [ Google Scholar ] [ CrossRef ]
• Kijmanawat, A.; Panburana, P.; Reutrakul, S.; Tangshewinsirikul, C. Efects of probiotic supplements on insulinresistance in gestational diabetes mellitus: A double-blind randomized controlled trial. J. Diabetes Investig. 2018 , 10 , 163–170. [ Google Scholar ] [ CrossRef ]
• Asemi, Z.; Samimi, M.; Tabassi, Z.; Esmaillzadeh, A. The effect of DASH diet on pregnancy outcomes in gestational diabetes: A randomized controlled clinical trial. Eur. J. Clin. Nutr. 2014 , 68 , 490–495. [ Google Scholar ] [ CrossRef ]
• Sarathi, V.; Kolly, A.; Chaithanya, H.B.; Dwarakanath, C.S. Effect of soya based protein rich diet on glycaemic parameters and thyroid function tests in women with gestational diabetes mellitus. Rom. J. Diabetes Nutr. Metab. Dis. 2016 , 23 , 201–208. [ Google Scholar ] [ CrossRef ]
• Guardo, F.D.; Curro, J.M.; Valenti, G.; Rossetti, P.; Di Gregorio, L.M.; Conway, F.; Chiofalo, B.; Garzon, S.; Bruni, S.; Rizzo, G. Non-pharmacological management of gestational diabetes: The role of myo-inositol. J. Complement. Integr. Med. 2019 , 17 . [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brown, J.; Crawford, T.J.; Alsweiler, J.; Crowther, C.A. Dietary supplementation with myo-inositol in women during pregnancy for treating gestational diabetes. Cochrane Database Syst. Rev. 2016 , 9 , CD012048. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brown, J.; Ceysens, G.; Boulvain, M. Exercise for pregnant women with gestational diabetes for improving maternal and fetal outcomes. Cochrane Database Syst. Rev. 2017 , 6 , CD012202. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• American College of Obstetricians and Gynecologists. ACOG Committee Opinion Number 650, December 2015. Physical Activity and Exercise During Pregnancy and the Postpartum Period. Available online: http://www.acog.org/Resources-And-Publications/Committee-Opinions/Committee-on-Obstetric-Practice/Physical-Activity-and-Exercise-During-Pregnancy-and-the-Postpartum-Period (accessed on 1 December 2015).
• Aune, D.; Sen, A.; Henriksen, T.; Saugstad, O.D.; Tonstad, S. Physical activity and the risk of gestational diabetes mellitus: A systemic review and dose-response meta-analysis of epidemiological studies. Eur. J. Epidemiol. 2016 , 31 , 967–997. [ Google Scholar ] [ CrossRef ]
• Nasiri-Amiri, F.; Sepidarkish, M.; Shirvani, M.A.; Habibipour, P.; Tabari, N.S. The effect of exercise on the prevention of gestational diabetes in obese and overweight pregnant women: A systemic review and meta-analysis. Diabetol. Metab. Syndr. 2019 , 11 , 72. [ Google Scholar ] [ CrossRef ]
• Ming, W.K.; Ding, W.; Zhang, C.J.; Zhong, L.; Long, Y.; Li, Z.; Sun, C.; Wu, Y.; Chen, H.; Chen, H.; et al. The effect of exercise during pregnancy on gestational diabetes mellitus in normal-weight women: A systemic review and meta-analysis. BMC Pregnancy Childbirth 2018 , 18 , 440. [ Google Scholar ] [ CrossRef ]
• Harrison, A.L.; Shields, N.; Taylor, N.; Frawley, H.C. Exercise improves glycaemic control in women diagnosed with gestational diabetes mellitus: A systematic review. J. Physiother. 2016 , 62 , 188–196. [ Google Scholar ] [ CrossRef ]
• Nguyen, L.; Chan, S.Y.; Teo, A.K. Metformin from mother to unborn child-are there unwarranted effects? EbioMedicine 2018 , 35 , 394–404. [ Google Scholar ] [ CrossRef ]
• Blum, A.K. Insulin Use in Pregnancy: An Update. Diabetes Spectr. 2016 , 29 , 92–97. [ Google Scholar ] [ CrossRef ]
• Hod, M.; Mathiesen, E.R.; Jovanovič, L.; McCance, D.R.; Ivanisevic, M.; Duran-Garcia, S.; Brøndsted, L.; Nazeri, A.; Damm, P. A randomized trial comparing perinatal outcomes using insulin detemir or neutral protamine Hagedorn in type 1 diabetes. J. Matern.-Fetal Neonatal Med. 2014 , 27 , 7–13. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Mathiesen, E.R.; Andersen, H.; Kring, S.I.; Damm, P. Design and rationale of a large, international, prospective cohort study to evaluate the occurrence of malformations and perinatal/neonatal death using insulin detemir in pregnant women with diabetes in comparison with other long-acting insulins. BMC Pregnancy Childbirth 2017 , 17 , 38. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Hod, M.; Damm, P.; Kaaja, R.; Visser, G.H.; Dunne, F.; Demidova, I.; Pade Hansen, A.-S.; Mersebach, H. Fetal and perinatal outcomes in type 1 diabetes pregnancy: A randomized study comparing insulin aspart with human insulin in 322 subjects. Am. J. Obstet. Gynecol. 2007 , 198 , 186-e1. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pantalone, K.M.; Failman, C.; Olansky, L. Insulin Glargine Use During Pregnancy. Endocr. Pract. 2011 , 17 , 448–455. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Brown, J.; Grzeskowiak, L.; Williamson, K.; Downie, M.R.; Crowther, C.A. Insulin for the treatment of women with gestational diabetes. Cochrane Database Syst. Rev. 2017 , 11 , CD012037. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Norgaard, K.; Sukumar, N.; Raffnson, S.B.; Saravanan, P. Efficacy and safety of rapid-acting insulin analogs in special populations with type 1 diabetes or gestational diabetes: Systemic review and meta-analysis. Diabetes Ther. 2018 , 9 , 891–917. [ Google Scholar ] [ CrossRef ]
• Mukerji, G.; Feig, D.S. Advances in Oral Anti-Diabetes Drugs in Pregnancy. Pract. Man. Diabetes Pregnancy 2017 , 15 , 189–201. [ Google Scholar ] [ CrossRef ]
• Lee, H.Y.; Wei, D.; Loeken, M.R. Lack of metformin effect on mouse embryo AMPK activity: Implications for metformin treatment during pregnancy. Diabetes/Metab. Res. Rev. 2014 , 30 , 23–30. [ Google Scholar ] [ CrossRef ]
• Rowan, J.A.; Hague, W.M.; Gao, W.; Battin, M.R.; Moore, M.P. Metformin versus insulin for the treatment of gestational diabetes. N. Engl. J. Med. 2008 , 358 , 2003–2015. [ Google Scholar ] [ CrossRef ]
• Rowan, J.; Rush, C.E.; Plank, L.D.; Lu, J.; Obolonkin, V.; Coat, S.; Hague, W.M. Metformin in gestational diabetes: The offspring follow-up (MiG TOFU): Body composition and metabolic outcomes at 7–9 years of age. BMJ Open Diabetes Res. Care 2018 , 6 , e000456. [ Google Scholar ] [ CrossRef ]
• Zeng, Y.; Li, M.; Chen, Y.; Jianng, L.; Wang, S.; Mo, X.; Li, B. The use of glyburide in the management of gestational diabetes mellitus: A meta-analysis. Adv. Med. Sci. 2014 , 59 , 95–101. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yu, D.-Q.; Xu, G.-X.; Teng, X.-Y.; Xu, J.-W.; Tang, L.-F.; Feng, C.; Rao, J.-P.; Jin, M.; Wang, L.-Q. Glycemic control and neonatal outcomes in women with gestational diabetes mellitus treated using glyburide, metformin, or insulin: A pairwise and network meta-analysis. BMC Endocr. Disord. 2021 , 21 , 199. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Schneeberger, C.; Kazemier, B.M.; Geerlings, S.E. Asymptomatic bacteriuria and urinary tract infections in special patient groups: Women with diabetes mellitus and pregnant women. Curr. Opin. Infect. Dis. 2014 , 27 , 108–114. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• White, W.B.; Cannon, C.P.; Heller, S.R.; Nissen, S.E.; Bergenstal, R.M.; Bakris, G.L.; Perez, A.T.; Fleck, P.R.; Mehta, C.R.; Kupfer, S.; et al. Alogliptin after acute coronary syndrome in patients with type 2 diabetes. N. Engl. J. Med. 2013 , 369 , 1327–1335. [ Google Scholar ] [ CrossRef ] [ PubMed ] [ Green Version ]
• Marso, S.P.; Daniels, G.H.; Brown-Frandsen, K.; Kristensen, P.; Mann, J.F.E.; Nauck, M.A.; Nissen, S.E.; Pocock, S.; Poulter, N.R.; Ravn, L.S.; et al. Liraglutide and cardiovascular outcomes in type 2 diabetes. N. Engl. J. Med. 2016 , 375 , 311–322. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chen, C.; Huang, Y.; Dong, G.; Zeng, Y.; Zhou, Z. The effect of dipeptidyl peptidase-4 inhibitor and glucagon-like peptide-1 receptor agonist in gestational diabetes mellitus: A systematic review. Gynecol. Endocrinol. 2020 , 36 , 375–380. [ Google Scholar ] [ CrossRef ]
• Simmons, D.; Nema, J.; Parton, C.; Vizza, L.; Robertson, A.; Rajagopal, R.; Ussher, J.; Perz, J. The treatment of booking gestational diabetes mellitus (TOBOGM) pilot randomised controlled trial. BMC Pregnancy Childbirth 2018 , 18 , 151. [ Google Scholar ] [ CrossRef ]

Share and Cite

Modzelewski, R.; Stefanowicz-Rutkowska, M.M.; Matuszewski, W.; Bandurska-Stankiewicz, E.M. Gestational Diabetes Mellitus—Recent Literature Review. J. Clin. Med. 2022 , 11 , 5736. https://doi.org/10.3390/jcm11195736

Modzelewski R, Stefanowicz-Rutkowska MM, Matuszewski W, Bandurska-Stankiewicz EM. Gestational Diabetes Mellitus—Recent Literature Review. Journal of Clinical Medicine . 2022; 11(19):5736. https://doi.org/10.3390/jcm11195736

Modzelewski, Robert, Magdalena Maria Stefanowicz-Rutkowska, Wojciech Matuszewski, and Elżbieta Maria Bandurska-Stankiewicz. 2022. "Gestational Diabetes Mellitus—Recent Literature Review" Journal of Clinical Medicine 11, no. 19: 5736. https://doi.org/10.3390/jcm11195736

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

• DOI: 10.1097/01.ogx.0000253303.92229.59
• Corpus ID: 13555049

Gestational Diabetes: A Review of the Current Literature and Guidelines

• M. Hollander , K. Paarlberg , +1 author M. Hollander
• Published in Obstetrical and Gynecological… 1 February 2007

134 Citations

Diabates in pregnancy: diagnosis and treatment. practice guidelines of turkish perinatology society, screening for gestational diabetes mellitus based on different risk profiles and settings for improving maternal and infant health., diagnosis and management of gestational diabetes mellitus., glycemic index and pregnancy: a systematic literature review, screening and diagnosing gestational diabetes mellitus revisited: implications from hapo, diabetes y embarazo, screening and subsequent management for gestational diabetes for improving maternal and infant health., is there a benefit to the treatment of mild gestational diabetes mellitus, austin endocrinology and diabetes case reports gestational diabetes mellitus: current perspectives, oral glucose tolerance testing outcomes among women at high risk for gestational diabetes mellitus.

• Highly Influenced
• 14 Excerpts

89 References

Screening for gestational diabetes mellitus: a critical review..

• Highly Influential

Gestational Diabetes:: A Field of Controversy

Screening for gestational diabetes: a systematic review and economic evaluation..

• 12 Excerpts

Management of gestational diabetes mellitus: a self-fulfilling prophecy?

Current controversies in the mechanisms and treatment of gestational diabetes, efficacy of screening for gestational diabetes., gestational diabetes: does the presence of risk factors influence perinatal outcome, screening for gestational diabetes: a summary of the evidence for the u.s. preventive services task force, treatments for gestational diabetes and impaired glucose tolerance in pregnancy., influence of pregnancy on the 75-g ogtt: a prospective multicenter study, related papers.

Showing 1 through 3 of 0 Related Papers

• - Google Chrome

Intended for healthcare professionals

• My email alerts
• BMA member login
• Username * Password * Forgot your log in details? Need to activate BMA Member Log In Log in via OpenAthens Log in via your institution

Search form

• Advanced search
• Search responses
• Search blogs
• Gestational diabetes...

Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis

• Related content
• Peer review
• Wenrui Ye , doctoral student 1 2 ,
• Cong Luo , doctoral student 3 ,
• Jing Huang , assistant professor 4 5 ,
• Chenglong Li , doctoral student 1 ,
• Zhixiong Liu , professor 1 2 ,
• 1 Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
• 2 Hypothalamic Pituitary Research Centre, Xiangya Hospital, Central South University, Changsha, China
• 3 Department of Urology, Xiangya Hospital, Central South University, Changsha, Hunan, China
• 4 National Clinical Research Centre for Mental Disorders, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
• 5 Department of Psychiatry, Second Xiangya Hospital, Central South University, Changsha, Hunan, China
• Correspondence to: F Liu liufangkun{at}csu.edu.cn
• Accepted 18 April 2022

Objective To investigate the association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for at least minimal confounding factors.

Design Systematic review and meta-analysis.

Data sources Web of Science, PubMed, Medline, and Cochrane Database of Systematic Reviews, from 1 January 1990 to 1 November 2021.

Review methods Cohort studies and control arms of trials reporting complications of pregnancy in women with gestational diabetes mellitus were eligible for inclusion. Based on the use of insulin, studies were divided into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. Subgroup analyses were performed based on the status of the country (developed or developing), quality of the study, diagnostic criteria, and screening method. Meta-regression models were applied based on the proportion of patients who had received insulin.

Results 156 studies with 7 506 061 pregnancies were included, and 50 (32.1%) showed a low or medium risk of bias. In studies with no insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and infant born large for gestational age (1.57, 1.25 to 1.97). In studies with insulin use, when adjusted for confounders, the odds of having an infant large for gestational age (odds ratio 1.61, 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31), were higher in women with gestational diabetes mellitus than in those without diabetes. No clear evidence was found for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and small for gestational age between women with and without gestational diabetes mellitus after adjusting for confounders. Country status, adjustment for body mass index, and screening methods significantly contributed to heterogeneity between studies for several adverse outcomes of pregnancy.

Conclusions When adjusted for confounders, gestational diabetes mellitus was significantly associated with pregnancy complications. The findings contribute to a more comprehensive understanding of the adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

Review registration PROSPERO CRD42021265837.

• Download figure
• Open in new tab
• Download powerpoint

Introduction

Gestational diabetes mellitus is a common chronic disease in pregnancy that impairs the health of several million women worldwide. 1 2 Formally recognised by O’Sullivan and Mahan in 1964, 3 gestational diabetes mellitus is defined as hyperglycaemia first detected during pregnancy. 4 With the incidence of obesity worldwide reaching epidemic levels, the number of pregnant women diagnosed as having gestational diabetes mellitus is growing, and these women have an increased risk of a range of complications of pregnancy. 5 Quantification of the risk or odds of possible adverse outcomes of pregnancy is needed for prevention, risk assessment, and patient education.

In 2008, the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) study recruited a large multinational cohort and clarified the risks of adverse outcomes associated with hyperglycaemia. The findings of the study showed that maternal hyperglycaemia independently increased the risk of preterm delivery, caesarean delivery, infants born large for gestational age, admission to a neonatal intensive care unit, neonatal hypoglycaemia, and hyperbilirubinaemia. 6 The obstetric risks associated with diabetes, such as pregnancy induced hypertension, macrosomia, congenital malformations, and neonatal hypoglycaemia, have been reported in several large scale studies. 7 8 9 10 11 12 The HAPO study did not adjust for some confounders, however, such as maternal body mass index, and did not report on stillbirths and neonatal respiratory distress syndrome, raising uncertainty about these outcomes. Other important pregnancy outcomes, such as preterm delivery, neonatal death, and low Apgar score in gestational diabetes mellitus, were poorly reported. No comprehensive study has assessed the relation between gestational diabetes mellitus and various maternal and fetal adverse outcomes after adjustment for confounders. Also, some cohort studies were restricted to specific clinical centres and regions, limiting their generalisation to more diverse populations.

By collating the available evidence, we conducted a systematic review and meta-analysis to quantify the short term outcomes in pregnancies complicated by gestational diabetes mellitus. We evaluated adjusted associations between gestational diabetes mellitus and various adverse outcomes of pregnancy.

This meta-analysis was conducted according to the recommendations of Cochrane Systematic Reviews, and our findings are reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (table S16). The study was prospectively registered in the international database of prospectively registered systematic reviews (PROSPERO CRD42021265837).

Search strategy and selection criteria

We searched the electronic databases PubMed, Web of Science, Medline, and the Cochrane Database of Systematic Reviews with the keywords: “pregnan*,” “gestatio*” or “matern*” together with “diabete*,” “hyperglycaemia,” “insulin,” “glucose,” or “glucose tolerance test*” to represent the exposed populations, and combined them with terms related to outcomes, such as “pregnan* outcome*,” “obstetric* complicat*,” “pregnan* disorder*,” “obstetric* outcome*,” “haemorrhage,” “induc*,” “instrumental,” “caesarean section,” “dystocia,” “hypertensi*,” “eclampsia,” “premature rupture of membrane,” “PROM,” “preter*,” “macrosomia,” and “malformation,” as well as some abbreviated diagnostic criteria, such as “IADPSG,” “DIPSI,” and “ADIPS” (table S1). The search strategy was appropriately translated for the other databases. We included observational cohort studies and control arms of trials, conducted after 1990, that strictly defined non-gestational diabetes mellitus (control) and gestational diabetes mellitus (exposed) populations and had definite diagnostic criteria for gestational diabetes mellitus (table S2) and various adverse outcomes of pregnancy.

Exclusion criteria were: studies published in languages other than English; studies with no diagnostic criteria for gestational diabetes mellitus (eg, self-reported gestational diabetes mellitus, gestational diabetes mellitus identified by codes from the International Classification of Diseases or questionnaires); studies published after 1990 that recorded pregnancy outcomes before 1990; studies of specific populations (eg, only pregnant women aged 30-34 years, 13 only twin pregnancies 14 15 16 ); studies with a sample size <300, because we postulated that these studies might not be adequate to detect outcomes within each group; and studies published in the form of an abstract, letter, or case report.

We also manually retrieved reference lists of relevant reviews or meta-analyses. Three reviewers (WY, CL, and JH) independently searched and assessed the literature for inclusion in our meta-analysis. The reviewers screened the titles and abstracts to exclude ineligible studies. The full texts of relevant records were then retrieved and assessed. Any discrepancies were resolved after discussion with another author (FL).

Data extraction

Three independent researchers (WY, CL, and JH) extracted data from the included studies with a predesigned form. If the data were not presented, we contacted the corresponding authors to request access to the data. We extracted data from the most recent study or the one with the largest sample size when a cohort was reported twice or more. Sociodemographic and clinical data were extracted based on: year of publication, location of the study (country and continent), design of the study (prospective or retrospective cohort), screening method and diagnostic criteria for gestational diabetes mellitus, adjustment for conventional prognostic factors (defined as maternal age, pregestational body mass index, gestational weight gain, gravidity, parity, smoking history, and chronic hypertension), and the proportion of patients with gestational diabetes mellitus who were receiving insulin. For studies that adopted various diagnostic criteria for gestational diabetes mellitus, we extracted the most recent or most widely accepted one for subsequent analysis. For studies adopting multivariate logistic regression for adjustment of confounders, we extracted adjusted odds ratios and synthesised them in subsequent analyses. For unadjusted studies, we calculated risk ratios and 95% confidence intervals based on the extracted data.

Studies of women with gestational diabetes mellitus that evaluated the risk or odds of maternal or neonatal complications were included. We assessed the maternal outcomes pre-eclampsia, induction of labour, instrumental delivery, caesarean section, shoulder dystocia, premature rupture of membrane, and postpartum haemorrhage. Fetal or neonatal outcomes assessed were stillbirth, neonatal death, congenital malformation, preterm birth, macrosomia, low birth weight, large for gestational age, small for gestational age, neonatal hypoglycaemia, neonatal jaundice, respiratory distress syndrome, low Apgar score, and admission to the neonatal intensive care unit. Table S3 provides detailed definitions of these adverse outcomes of pregnancy.

Risk-of-bias assessment

A modified Newcastle-Ottawa scale was used to assess the methodological quality of the selection, comparability, and outcome of the included studies (table S4). Three independent reviewers (WY, CL, and JH) performed the quality assessment and scored the studies for adherence to the prespecified criteria. A study that scored one for selection or outcome, or zero for any of the three domains, was considered to have a high risk of bias. Studies that scored two or three for selection, one for comparability, and two for outcome were regarded as having a medium risk of bias. Studies that scored four for selection, two for comparability, and three for outcome were considered to have a low risk of bias. A lower risk of bias denotes higher quality.

Data synthesis and analysis

Pregnant women were divided into two groups (gestational diabetes mellitus and non-gestational diabetes mellitus) based on the diagnostic criteria in each study. Studies were considered adjusted if they adjusted for at least one of seven confounding factors (maternal age, pregestational body mass index, gestational weight gain, gravidity, parity, smoking history, and chronic hypertension). For each adjusted study, we transformed the odds ratio estimate and its corresponding standard error to natural logarithms to stabilise the variance and normalise their distributions. Summary odds ratio estimates and their 95% confidence intervals were estimated by a random effects model with the inverse variance method. We reported the results as odds ratio with 95% confidence intervals to reflect the uncertainty of point estimates. Unadjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy were quantified and summarised (table S6 and table S14). Thereafter, heterogeneity across the studies was evaluated with the τ 2 statistics and Cochran’s Q test. 17 18 Cochran’s Q test assessed interactions between subgroups. 18

We performed preplanned subgroup analyses for factors that could potentially affect gestational diabetes mellitus or adverse outcomes of pregnancy: country status (developing or developed country according to the International Monetary Fund ( www.imf.org/external/pubs/ft/weo/2020/01/weodata/groups.htm ), risk of bias (low, medium, or high), screening method (universal one step, universal glucose challenge test, or selective screening based on risk factors), diagnostic criteria for gestational diabetes mellitus (World Health Organization 1999, Carpenter-Coustan criteria, International Association of Diabetes and Pregnancy Study Groups (IADPSG), or other), and control for body mass index. We assessed small study effects with funnel plots by plotting the natural logarithm of the odds ratios against the inverse of the standard errors, and asymmetry was assessed with Egger’s test. 19 A meta-regression model was used to investigate the associations between study effect size and proportion of patients who received insulin in the gestational diabetes mellitus population. Next, we performed sensitivity analyses by omitting each study individually and recalculating the pooled effect size estimates for the remaining studies to assess the effect of individual studies on the pooled results. All analyses were performed with R language (version 4.1.2, www.r-project.org ) and meta package (version 5.1-0). We adopted the treatment arm continuity correction to deal with a zero cell count 20 and the Hartung-Knapp adjustment for random effects meta models. 21 22

Patient and public involvement

The experience in residency training in the department of obstetrics and the concerns about the association between gestational diabetes mellitus and health outcomes inspired the author team to perform this study. We also asked advice from the obstetrician and patients with gestational diabetes mellitus about which outcomes could be included. The covid-19 restrictions meant that we sought opinions from only a limited number of patients in outpatient settings.

Characteristics of included studies

Of the 44 993 studies identified, 156 studies, 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 involving 7 506 061 pregnancies, were eligible for the analysis of adverse outcomes in pregnancy ( fig 1 ). Of the 156 primary studies, 133 (85.3%) reported maternal outcomes and 151 (96.8%) reported neonatal outcomes. Most studies were conducted in Asia (39.5%), Europe (25.5%), and North America (15.4%). Eighty four (53.8%) studies were performed in developed countries. Based on the Newcastle-Ottawa scale, 50 (32.1%) of the 156 included studies showed a low or medium risk of bias and 106 (67.9%) had a high risk of bias. Patients in 35 (22.4%) of the 156 studies never used insulin during the course of the disease and 63 studies (40.4%) reported treatment with insulin in different proportions of patients. The remaining 58 studies did not report information about the use of insulin. Table 1 summarises the characteristics of the study population, including continent or region, country, screening methods, and diagnostic criteria for the included studies. Table S5 lists the key excluded studies.

Search and selection of studies for inclusion

Characteristics of study population

• View inline

Associations between gestational diabetes mellitus and adverse outcomes of pregnancy

Based on the use of insulin in each study, we classified the studies into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. We reported odds ratios with 95% confidence intervals after controlling for at least minimal confounding factors. In studies with no insulin use, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and an infant born large for gestational age (1.57, 1.25 to 1.97) ( fig 2 and fig S1). In studies with insulin use, adjusted for confounders, the odds of an infant born large for gestational age (odds ratio 1.61, 95% confidence interval 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31) were higher in women with than in those without gestational diabetes mellitus ( fig 3) . In studies that did not report the use of insulin, women with gestational diabetes mellitus had increased odds ratio for pre-eclampsia (1.46, 1.21 to 1.78), induction of labour (1.88, 1.16 to 3.04), caesarean section (1.38, 1.20 to 1.58), premature rupture of membrane (1.13, 1.06 to 1.20), congenital malformation (1.18, 1.10 to 1.26), preterm delivery (1.51, 1.19 to 1.93), macrosomia (1.48, 1.13 to 1.95), neonatal hypoglycaemia (11.71, 7.49 to 18.30), and admission to the neonatal intensive care unit (2.28, 1.26 to 4.13) (figs S3 and S4). We found no clear evidence for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and infant born small for gestational age between women with and without gestational diabetes mellitus in all three subgroups ( fig 2, fig 3, and figs S1-S4). Table S6 shows the unadjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy.

Findings of meta-analysis of association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjusting for at least minimal confounding factors, in studies in patients who never used insulin during the course of the disease (no insulin use). NA=not applicable

Findings of meta-analysis of association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjusting for at least minimal confounding factors, in studies where different proportions of patients were treated with insulin (insulin use). NA=not applicable

Subgroup, meta-regression, and sensitivity analyses

Subgroup analyses, based on risk of bias, did not show significant heterogeneity between the subgroups of women with and without gestational diabetes mellitus for most adverse outcomes of pregnancy ( table 2 and table 3 ), except for admission to the neonatal intensive care unit in studies where insulin use was not reported (table S7). Significant differences between subgroups were reported for country status and macrosomia in studies with (P<0.001) and without (P=0.001) insulin use ( table 2 and table 3 ), and for macrosomia (P=0.02) and infants born large for gestational age (P<0.001) based on adjustment for body mass index in studies with insulin use (table S8). Screening methods contributed significantly to the heterogeneity between studies for caesarean section (P<0.001) and admission to the neonatal intensive care unit (P<0.001) in studies where insulin use was not reported (table S7). In most outcomes, the estimated odds were lower in studies that used universal one step screening than those that adopted the universal glucose challenge test or selective screening methods ( table 2 and table 3 ). Diagnostic criteria were not related to heterogeneity between the studies for all of the study subgroups (no insulin use, insulin use, insulin use not reported). The subgroup analysis was performed only for outcomes including ≥6 studies.

Subgroup analysis according to country status, diagnostic criteria, screening method, and risk of bias for adverse outcomes of pregnancy in women with gestational diabetes mellitus compared with women without gestational diabetes mellitus in studies with no insulin use

Subgroup analysis according to country status, diagnostic criteria, screening method, and risk of bias for adverse outcomes of pregnancy in women with gestational diabetes mellitus compared with women without gestational diabetes mellitus in studies with insulin use

We applied meta-regression models to evaluate the modification power of the proportion of patients with insulin use when sufficient data were available. Significant associations were found between effect size estimate and proportion of patients who had received insulin for the adverse outcomes caesarean section (estimate=0.0068, P=0.04) and preterm delivery (estimate=−0.0069, P=0.04) (table S9).

In sensitivity analyses, most pooled estimates were not significantly different when a study was omitted, suggesting that no one study had a large effect on the pooled estimate. The pooled estimate effect became significant (P=0.005) for low birth weight when the study of Lu et al 99 was omitted, however (fig S5). We found evidence of a small study effect only for caesarean section (Egger’s P=0.01, table S10). Figure S6 shows the funnel plots of the included studies for various adverse outcomes (≥10 studies).

Principal findings

We have provided quantitative estimates for the associations between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for confounding factors, through a systematic search and comprehensive meta-analysis. Compared with patients with normoglycaemia during pregnancy, patients with gestational diabetes mellitus had increased odds of caesarean section, preterm delivery, low one minute Apgar score, macrosomia, and an infant born large for gestational age in studies where insulin was not used. In studies with insulin use, patients with gestational diabetes mellitus had an increased odds of an infant born large for gestational age, or with respiratory distress syndrome or neonatal jaundice, or requiring admission to the neonatal intensive care unit. Our study was a comprehensive analysis, quantifying the adjusted associations between gestational diabetes mellitus and adverse outcomes of pregnancy. The study provides updated critical information on gestational diabetes mellitus and adverse outcomes of pregnancy and would facilitate counselling of women with gestational diabetes mellitus before delivery.

To examine the heterogeneity conferred by different severities of gestational diabetes mellitus, we categorised the studies by use of insulin. Insulin is considered the standard treatment for the management of gestational diabetes mellitus when adequate glucose levels are not achieved with nutrition and exercise. 179 Our meta-regression showed that the proportion of patients who had received insulin was significantly associated with the effect size estimate of adverse outcomes, including caesarean section (P=0.04) and preterm delivery (P=0.04). This finding might be the result of a positive linear association between glucose concentrations and adverse outcomes of pregnancy, as previously reported. 180 However, the proportion of patients who were receiving insulin indicates the percentage of patients with poor glycaemic control in the population and cannot reflect glycaemic control at the individual level.

Screening methods for gestational diabetes mellitus have changed over time, from the earliest selective screening (based on risk factors) to universal screening by the glucose challenge test or the oral glucose tolerance test, recommended by the US Preventive Services Task Force (2014) 181 and the American Diabetes Association (2020). 182 The diagnostic accuracy of these screening methods varied, contributing to heterogeneity in the analysis.

Several studies have tried to pool the effects of gestational diabetes mellitus on pregnancy outcomes, but most focused on one outcome, such as congenital malformations, 183 184 macrosomia, 185 186 or respiratory distress syndrome. 187 Our findings of increased odds of macrosomia in gestational diabetes mellitus in studies where insulin was not used, and respiratory distress syndrome in studies with insulin use, were similar to the results of previous meta-analyses. 188 189 The increased odds of neonatal respiratory distress syndrome, along with low Apgar scores, might be attributed to disruption of the integrity and composition of fetal pulmonary surfactant because gestational diabetes mellitus can delay the secretion of phosphatidylglycerol, an essential lipid component of surfactants. 190

Although we detected no significant association between gestational diabetes mellitus and mortality events, the observed increase in the odds of neonatal death (odds ratio 1.59 in studies that did not report the use of insulin) should be emphasised to obstetricians and pregnant women because its incidence was low (eg, 3.75% 87 ). The increased odds of neonatal death could result from several lethal complications, such as respiratory distress syndrome, neonatal hypoglycaemia (3.94-11.71-fold greater odds), and jaundice. These respiratory and metabolic disorders might increase the likelihood of admission to the neonatal intensive care unit.

For the maternal adverse outcomes, women with gestational diabetes mellitus had increased odds of pre-eclampsia, induction of labour, and caesarean section, consistent with findings in previous studies. 126 Our study identified a 1.24-1.46-fold greater odds of pre-eclampsia between patients with and without gestational diabetes mellitus, which was similar to previous results. 191

Strengths and limitations of the study

Our study included more studies than previous meta-analyses and covered a range of maternal and fetal outcomes, allowing more comprehensive comparisons among these outcomes based on the use of insulin and different subgroup analyses. The odds of adverse fetal outcomes, including respiratory distress syndrome (P=0.002), neonatal jaundice (P=0.05), and admission to the neonatal intensive care unit (P=0.005), were significantly increased in studies with insulin use, implicating their close relation with glycaemic control. The findings of this meta-analysis support the need for an improved understanding of the pathophysiology of gestational diabetes mellitus to inform the prediction of risk and for precautions to be taken to reduce adverse outcomes of pregnancy.

The study had some limitations. Firstly, adjustment for at least one confounder had limited power to deal with potential confounding effects. The set of adjustment factors was different across studies, however, and defining a broader set of multiple adjustment variables was difficult. This major concern should be looked at in future well designed prospective cohort studies, where important prognostic factors are controlled. Secondly, overt diabetes was not clearly defined until the IADPSG diagnostic criteria were proposed in 2010. Therefore, overt diabetes or pre-existing diabetes might have been included in the gestational diabetes mellitus groups if studies were conducted before 2010 or adopted earlier diagnostic criteria. Hence we cannot rule out that some adverse effects in newborns were related to prolonged maternal hyperglycaemia. Thirdly, we divided and analysed the subgroups based on insulin use because insulin is considered the standard treatment for the management of gestational diabetes mellitus and can reflect the level of glycaemic control. Accurately determining the degree of diabetic control in patients with gestational diabetes mellitus was difficult, however. Finally, a few pregnancy outcomes were not accurately defined in studies included in our analysis. Stillbirth, for example, was defined as death after the 20th or 28th week of pregnancy, based on different criteria, but some studies did not clearly state the definition of stillbirth used in their methods. Therefore, we considered stillbirth as an outcome based on the clinical diagnosis in the studies, which might have caused potential bias in the analysis.

Conclusions

We performed a meta-analysis of the association between gestational diabetes mellitus and adverse outcomes of pregnancy in more than seven million women. Gestational diabetes mellitus was significantly associated with a range of pregnancy complications when adjusted for confounders. Our findings contribute to a more comprehensive understanding of adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.

What is already known on this topic

The incidence of gestational diabetes mellitus is gradually increasing and is associated with a range of complications for the mother and fetus or neonate

Pregnancy outcomes in gestational diabetes mellitus, such as neonatal death and low Apgar score, have not been considered in large cohort studies

Comprehensive systematic reviews and meta-analyses assessing the association between gestational diabetes mellitus and adverse pregnancy outcomes are lacking

What this study adds

This systematic review and meta-analysis showed that in studies where insulin was not used, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean delivery, preterm delivery, low one minute Apgar score, macrosomia, and an infant large for gestational age in the pregnancy outcomes

In studies with insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of an infant large for gestational age, or with respiratory distress syndrome or neonatal jaundice, or requiring admission to the neonatal intensive care unit

Future primary studies should routinely consider adjusting for a more complete set of prognostic factors

Ethics statements

Ethical approval.

Not required.

Data availability statement

Table S11 provides details of adjustment for core confounders. Supplementary data files contain all of the raw tabulated data for the systematic review (table S12). Tables S13-15 provide the raw data and R language codes used for the meta-analysis.

Contributors: WY and FL developed the initial idea for the study, designed the scope, planned the methodological approach, wrote the computer code and performed the meta-analysis. WY and CL coordinated the systematic review process, wrote the systematic review protocol, completed the PROSPERO registration, and extracted the data for further analysis. ZL coordinated the systematic review update. WY, JH, and FL defined the search strings, executed the search, exported the results, and removed duplicate records. WY, CL, ZL, and FL screened the abstracts and texts for the systematic review, extracted relevant data from the systematic review articles, and performed quality assessment. WY, ZL, and FL wrote the first draft of the manuscript and all authors contributed to critically revising the manuscript. ZL and FL are the study guarantors. ZL and FL are senior and corresponding authors who contributed equally to this study. All authors had full access to all the data in the study, and the corresponding authors had final responsibility for the decision to submit for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

Funding: The research was funded by the National Natural Science Foundation of China (grants 82001223 and 81901401), and the Natural Science Foundation for Young Scientist of Hunan Province, China (grant 2019JJ50952). The funders had no role in considering the study design or in the collection, analysis, interpretation of data, writing of the report, or decision to submit the article for publication.

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/disclosure-of-interest/ and declare: support from the National Natural Science Foundation of China and the Natural Science Foundation for Young Scientist of Hunan Province, China for the submitted work; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

The lead author (the manuscript’s guarantor) affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Dissemination to participants and related patient and public communities: The dissemination plan targets a wide audience, including members of the public, patients, patient and public communities, health professionals, and experts in the specialty through various channels: written communication, events and conferences, networks, and social media.

Provenance and peer review: Not commissioned; externally peer reviewed.

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .

• Saravanan P ,
• Diabetes in Pregnancy Working Group ,
• Maternal Medicine Clinical Study Group ,
• Royal College of Obstetricians and Gynaecologists, UK
• O’Sullivan JB ,
• Hartling L ,
• Dryden DM ,
• Guthrie A ,
• Vandermeer B ,
• McIntyre HD ,
• Catalano P ,
• Mathiesen ER ,
• Metzger BE ,
• HAPO Study Cooperative Research Group
• Persson M ,
• Balsells M ,
• García-Patterson A ,
• Simmonds M ,
• Murphy HR ,
• Roland JM ,
• East Anglia Study Group for Improving Pregnancy Outcomes in Women with Diabetes (EASIPOD)
• Magnuson A ,
• Simmons D ,
• Sumeksri P ,
• Wongyai S ,
• González González NL ,
• Bellart J ,
• Guillén MA ,
• Herranz L ,
• Barquiel B ,
• Hillman N ,
• Burgos MA ,
• Pallardo LF
• Higgins JP ,
• Thompson SG ,
• Davey Smith G ,
• Schneider M ,
• Sweeting MJ ,
• Sutton AJ ,
• Hartung J ,
• IntHout J ,
• Ioannidis JP ,
• Alberico S ,
• Montico M ,
• Barresi V ,
• Multicentre Study Group on Mode of Delivery in Friuli Venezia Giulia
• Alfadhli EM ,
• Anderberg E ,
• Ardawi MS ,
• Nasrat HA ,
• Al-Sagaaf HM ,
• Kenealy T ,
• Barakat MN ,
• Youssef RM ,
• Al-Lawati JA
• Ibrahim I ,
• Eltaher F ,
• Benhalima K ,
• Hanssens M ,
• Devlieger R ,
• Verhaeghe J ,
• Berggren EK ,
• Boggess KA ,
• Stuebe AM ,
• Jonsson Funk M
• Korucuoglu U ,
• Aksakal N ,
• Himmetoglu O
• Bodmer-Roy S ,
• Cousineau J ,
• Broekman BFP ,
• GUSTO study group
• Catalano PM ,
• Cruickshank JK ,
• Chanprapaph P ,
• Cheung NW ,
• Lopez-Rodo V ,
• Rodriguez-Vaca D ,
• Benchimol M ,
• Carbillon L ,
• Benbara A ,
• Pharisien I ,
• Scifres CM ,
• Gorban de Lapertosa S ,
• Salzberg S ,
• DPSG-SAD Group
• Zijlmans AB ,
• Rademaker D ,
• Djelmis J ,
• Mulliqi Kotori V ,
• Pavlić Renar I ,
• Ivanisevic M ,
• Oreskovic S
• Domanski G ,
• Ittermann T ,
• Donovan LE ,
• Edwards AL ,
• Torrejón MJ ,
• Ekeroma AJ ,
• Chandran GS ,
• McCowan L ,
• Eagleton C ,
• Erjavec K ,
• Poljičanin T ,
• Matijević R
• Ethridge JK Jr . ,
• Feleke BE ,
• Feleke TE ,
• Forsbach G ,
• Cantú-Diaz C ,
• Vázquez-Lara J ,
• Villanueva-Cuellar MA ,
• Alvarez y García C ,
• Rodríguez-Ramírez E
• Gonçalves E ,
• Gortazar L ,
• Flores-Le Roux JA ,
• Benaiges D ,
• Gruendhammer M ,
• Brezinka C ,
• Lechleitner M
• Hedderson MM ,
• Ferrara A ,
• Hillier TA ,
• Pedula KL ,
• Morris JM ,
• Hossein-Nezhad A ,
• Maghbooli Z ,
• Vassigh AR ,
• Massaro N ,
• Streckeisen S ,
• Ikenoue S ,
• Miyakoshi K ,
• Raghav SK ,
• Jensen DM ,
• Sørensen B ,
• Rich-Edwards JW ,
• Kreisman S ,
• Tildesley H
• Kachhwaha CP ,
• Kautzky-Willer A ,
• Bancher-Todesca D ,
• Weitgasser R ,
• Keikkala E ,
• Mustaniemi S ,
• Koivunen S ,
• Keshavarz M ,
• Babaee GR ,
• Moghadam HK ,
• Kgosidialwa O ,
• Carmody L ,
• Gunning P ,
• Kieffer EC ,
• Carman WJ ,
• Sanborn CZ ,
• Viljakainen M ,
• Männistö T ,
• Kachhawa G ,
• Laafira A ,
• Griffin CJ ,
• Johnson JA ,
• Lapolla A ,
• Dalfrà MG ,
• Ragazzi E ,
• De Cata AP ,
• Norwitz E ,
• Leybovitz-Haleluya N ,
• Wainstock T ,
• Hinkle SN ,
• Grantz KL ,
• Lopez-de-Andres A ,
• Carrasco-Garrido P ,
• Gil-de-Miguel A ,
• Hernandez-Barrera V ,
• Jiménez-García R
• Luengmettakul J ,
• Sunsaneevithayakul P ,
• Talungchit P
• Macaulay S ,
• Munthali RJ ,
• Dunger DB ,
• Makwana M ,
• Bhimwal RK ,
• El Mallah KO ,
• Kulaylat NA ,
• Melamed N ,
• Vandenberghe H ,
• Jensen RC ,
• Kibusi SM ,
• Munyogwa MJ ,
• Patient C ,
• Miailhe G ,
• Legardeur H ,
• Mandelbrot L
• Minsart AF ,
• N’guyen TS ,
• Ratsimandresy R ,
• Ali Hadji R
• Matsumoto T ,
• Morikawa M ,
• Sugiyama T ,
• Knights SJ ,
• Olayemi OO ,
• Mwanri AW ,
• Ramaiya K ,
• Abalkhail B ,
• Nguyen TH ,
• Nguyen CL ,
• Minh Pham N ,
• Nicolosi BF ,
• Vernini JM ,
• Kragelund Nielsen K ,
• Andersen GS ,
• Nybo Andersen AM
• Ogonowski J ,
• Miazgowski T ,
• Czeszyńska MB ,
• Kuczyńska M ,
• Miazgowski T
• Morrish DW ,
• O’Sullivan EP ,
• O’Reilly M ,
• Dennedy MC ,
• Gaffney G ,
• Atlantic DIP collaborators
• Ovesen PG ,
• Rasmussen S ,
• Kesmodel US
• Ozumba BC ,
• Premuzic V ,
• Zovak Pavic A ,
• Bevanda M ,
• Mihaljevic S ,
• Ramachandran A ,
• Snehalatha C ,
• Clementina M ,
• Sasikala R ,
• Redman LM ,
• LIFE-Moms Research Group
• Spanish Group for the Study of the Impact of Carpenter and Coustan GDM thresholds
• Bowker SL ,
• Montoro MN ,
• Lawrence JM
• Kamalanathan S ,
• Saldana TM ,
• Siega-Riz AM ,
• Savitz DA ,
• Thorp JM Jr .
• Savona-Ventura C ,
• Schwartz ML ,
• Lubarsky SL
• Segregur J ,
• Buković D ,
• Milinović D ,
• Cavkaytar S ,
• Shahbazian H ,
• Nouhjah S ,
• Shahbazian N ,
• McElduff A ,
• Sheffield JS ,
• Butler-Koster EL ,
• McIntire DD ,
• Saigusa Y ,
• Nakanishi S ,
• Templeton A ,
• Sirimarco MP ,
• Guerra HM ,
• Lisboa EG ,
• Sletner L ,
• Yajnik CS ,
• Soliman A ,
• Al Rifai H ,
• Soonthornpun S ,
• Soonthornpun K ,
• Aksonteing J ,
• Thamprasit A
• Srichumchit S ,
• Sugiyama MS ,
• Roseveare C ,
• Basilius K ,
• Madraisau S
• Hansen BB ,
• Mølsted-Pedersen L
• Caswell A ,
• Holliday E ,
• Petrović O ,
• Crnčević Orlić Ž ,
• Vambergue A ,
• Nuttens MC ,
• Goeusse P ,
• Biausque S ,
• van Hoorn J ,
• von Katterfeld B ,
• McNamara B ,
• Langridge AT
• Wahabi HA ,
• Esmaeil SA ,
• Alzeidan RA
• Esmaeil S ,
• Mamdouh H ,
• Wahlberg J ,
• Nyström L ,
• Persson B ,
• Arnqvist HJ
• Nankervis A ,
• Weijers RN ,
• Bekedam DJ ,
• Smulders YM
• Bleicher K ,
• Saunders LD ,
• Demianczuk NN
• Homer CSE ,
• Sullivan EA
• American Diabetes Association
• U.S. Preventive Services Task Force
• Tabrizi R ,
• Lankarani KB ,
• Leung-Pineda V ,
• Gronowski AM
• Bryson CL ,
• Ioannou GN ,
• Rulyak SJ ,
• Critchlow C

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Gestational diabetes mellitus - literature review on selected cytokines and hormones of confirmed or possible role in its pathogenesis

Affiliation.

• 1 Chair of Internal Medicine and Department of Internal Medicine in Nursing, Medical University in Lublin, Lublin, Poland, 8 Jaczewskiego Street, 20-954 Lublin, Poland; Department of Endocrinology, Medical University in Lublin, Lublin, Poland, 8 Jaczewskiego Street, 20-954 Lublin, Poland. [email protected].
• PMID: 30318581
• DOI: 10.5603/GP.a2018.0089

The incidence of gestational diabetes mellitus (GDM) increases globally, including Poland. Considering serious consequences of gestational diabetes for both mother and fetus, screening for this disorder is an obligatory element of managing pregnant woman. The pathogenesis of gestational diabetes is not yet thoroughly explained. However, it is insulin resistance and chronic subclinical inflammatory process which are considered to be major factors responsible for the development of GDM. These two states are triggered mainly by secretion of proinflammatory cytokines and by abnormal function of adipose tissue. The study reviews the literature on selected hormones and cytokines whose role in the GDM pathogenesis has been already confirmed as well as on those proteins whose role is either not yet fully understood or which may possibly participate in GDM development. Owing to the fact that underlying mechanisms of GDM are, in general, similar to the mechanisms responsible for metabolic disorders such as diabetes mellitus type 2 or obesity, in this review we focus first on the role these molecules play in pathogenesis of metabolic disorders and then present current state of knowledge on their action in gestational diabetes development. The review presents: TNF alpha, adipokines - adiponectin and leptin and relatively newly discovered proteins: fetuin A, periostin, angiopoietin-like protein 8 or high mobility group box.

Keywords: cytokines; gestational diabetes; hormones; metabolic disorders.

PubMed Disclaimer

Similar articles

• The role of visfatin in pathogenesis of gestational diabetes (GDM). Radzicka S, Pietryga M, Iciek R, Brązert J. Radzicka S, et al. Ginekol Pol. 2018;89(9):518-521. doi: 10.5603/GP.a2018.0088. Ginekol Pol. 2018. PMID: 30318580 Review.
• GDM-complicated pregnancies: focus on adipokines. Mallardo M, Ferraro S, Daniele A, Nigro E. Mallardo M, et al. Mol Biol Rep. 2021 Dec;48(12):8171-8180. doi: 10.1007/s11033-021-06785-0. Epub 2021 Oct 15. Mol Biol Rep. 2021. PMID: 34652617 Free PMC article. Review.
• The relationship between adipose tissue-derived hormones and gestational diabetes mellitus (GDM). Wójcik M, Chmielewska-Kassassir M, Grzywnowicz K, Woźniak L, Cypryk K. Wójcik M, et al. Endokrynol Pol. 2014;65(2):134-42. doi: 10.5603/EP.2014.0019. Endokrynol Pol. 2014. PMID: 24802737 Review.
• Circulating adipokines and mRNA expression in adipose tissue and the placenta in women with gestational diabetes mellitus. Tsiotra PC, Halvatsiotis P, Patsouras K, Maratou E, Salamalekis G, Raptis SA, Dimitriadis G, Boutati E. Tsiotra PC, et al. Peptides. 2018 Mar;101:157-166. doi: 10.1016/j.peptides.2018.01.005. Epub 2018 Jan 11. Peptides. 2018. PMID: 29337272 Clinical Trial.
• Comparison of serum vaspin levels and vaspin expression in adipose tissue and smooth muscle tissue in pregnant women with and without gestational diabetes. Tang Y, Qiao P, Qu X, Bao Y, Li Y, Liao Y, Ying H. Tang Y, et al. Clin Endocrinol (Oxf). 2017 Oct;87(4):344-349. doi: 10.1111/cen.13403. Epub 2017 Jul 24. Clin Endocrinol (Oxf). 2017. PMID: 28636750
• The Effect of Vitamin D3 Intervention on the Association Among Vitamin D3, Adiponectin, and Body Mass Index in Pregnant Women With Gestational Diabetes. Nadeem A, Saeed M, Sadiqa A, Moin H, Khan QU. Nadeem A, et al. Cureus. 2023 Aug 15;15(8):e43506. doi: 10.7759/cureus.43506. eCollection 2023 Aug. Cureus. 2023. PMID: 37719527 Free PMC article.
• The Incidence and Management of Type 2 Diabetes Mellitus After Gestational Diabetes Mellitus. Ikoh Rph CL, Tang Tinong R. Ikoh Rph CL, et al. Cureus. 2023 Aug 31;15(8):e44468. doi: 10.7759/cureus.44468. eCollection 2023 Aug. Cureus. 2023. PMID: 37664380 Free PMC article. Review.
• The downregulation of miR-22 and miR-372 may contribute to gestational diabetes mellitus through regulating glucose metabolism via the PI3K/AKT/GLUT4 pathway. Li W, Yuan X, He X, Yang L, Wu Y, Deng X, Zeng Y, Hu K, Tang B. Li W, et al. J Clin Lab Anal. 2022 Jul;36(7):e24557. doi: 10.1002/jcla.24557. Epub 2022 Jun 17. J Clin Lab Anal. 2022. PMID: 35712865 Free PMC article.
• Effect of dietary pattern on pregnant women with gestational diabetes mellitus and its clinical significance. Wang J, Xie Z, Chen P, Wang Y, Li B, Dai F. Wang J, et al. Open Life Sci. 2022 Mar 19;17(1):202-207. doi: 10.1515/biol-2022-0006. eCollection 2022. Open Life Sci. 2022. PMID: 35415242 Free PMC article.
• Novel Biomolecules in the Pathogenesis of Gestational Diabetes Mellitus. Ruszała M, Niebrzydowska M, Pilszyk A, Kimber-Trojnar Ż, Trojnar M, Leszczyńska-Gorzelak B. Ruszała M, et al. Int J Mol Sci. 2021 Oct 27;22(21):11578. doi: 10.3390/ijms222111578. Int J Mol Sci. 2021. PMID: 34769010 Free PMC article. Review.

Publication types

• Search in MeSH

Related information

• PubChem Compound (MeSH Keyword)

LinkOut - more resources

Full text sources.

• Via Medica Medical Publishers

• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.7(1); 2020 Jan

Guidelines for the nursing management of gestational diabetes mellitus: An integrative literature review

Gwendolyn patience mensah.

1 School of Nursing and Midwifery, College of Health Sciences, University of Ghana, Ghana Legon

Wilma ten Ham‐Baloyi

2 Faculty of Health Sciences, Nelson Mandela University, Port Elizabeth South Africa

Dalena (R.M.) van Rooyen

Sihaam jardien‐baboo.

3 Department of Nursing Science, Nelson Mandela University, Port Elizabeth South Africa

Aims and objectives

An integrative literature review searched for, selected, appraised, extracted and synthesized data from existing available guidelines on the nursing management of gestational diabetes mellitus as no such analysis has been found.

Early screening, diagnosis and management of gestational diabetes mellitus are important to prevent or reduce complications during and postpregnancy for both mother and child. A variety of guidelines exists, which assist nurses and midwives in the screening, diagnosis and management of gestational diabetes mellitus.

An integrative literature review.

The review was conducted in June 2018 following an extensive search of available guidelines according to an adaptation of the stages reported by Whittemore and Knafl (2005, Journal of Advanced Nursing , 52, 546). Thus, a five‐step process was used, namely formulation of the review question, literature search, critical appraisal of guidelines identified, data extraction and data analysis. All relevant guidelines were subsequently appraised for rigour and quality by two independent reviewers using the AGREE II tool. Content analysis was used analysing the extracted data.

Following extraction and analysis of data, two major themes were identified from eighteen ( N  = 18) guidelines. These were the need for early screening and diagnosis of gestational diabetes mellitus and for nursing management of gestational diabetes mellitus (during pregnancy, intra‐ and postpartum management). Various guidelines on the nursing management of gestational diabetes mellitus were found; however, guidelines were not always comprehensive, sometimes differed in their recommended practices and did not consider a variety of contextual barriers to the implementation of the recommendations.

Critically, scrutiny of the guidelines is required, both in terms of the best evidence used in their development and in terms of the feasibility of implementation for its context.

Relevance to clinical practice

This study provides a summary of best practices regarding the diagnosis, screening and nursing management of gestational diabetes mellitus that provide guidance for nurse–midwives on maternal and postpartum follow‐up care for women at risk or diagnosed with gestational diabetes mellitus.

1. INTRODUCTION

The prevalence of gestational diabetes mellitus (GDM) varies per country but is estimated to be approximately 15% among pregnant women globally (Zhu & Zhang, 2016 ). However, the global prevalence is expected to increase due to increasing numbers of overweight and obese women of reproductive age (Guariguata, Linnenkamp, Beagley, Whithing, & Cho, 2014 ; Kampmann et al., 2015 ). During 2003–2014, the prevalence of pregnant women with overweight and obesity increased in high middle‐income countries mainly due to increased caloric supply and urbanization and in upper middle‐ and lower middle‐income countries as a result of the decreased employment of women in agricultural activities (Chen, Xu, & Yan, 2018 ). GDM is defined as any degree of glucose intolerance with onset or first recognition during pregnancy (American Diabetes Association [ADA], 2010 ). GDM characterizes the most common metabolic complication of pregnancy and is related to maternal complications such as hypertension, pre‐eclampsia, caesarean section, infection and polyhydramnios. It is also related to foetal morbidity in terms of macrosomia, birth trauma, hypoglycaemia, hypocalcaemia, hypomagnesemia, hyperbilirubinemia, respiratory distress syndrome and polycythemia (Mitanchez, Yzydorczyk, & Simeoni, 2015 ; Rafiq, Hussain, Jan, & Najar, 2015 ).

Additionally, women diagnosed with GDM are considerably more at risk for impaired glucose tolerance and are up to six times more likely to develop type 2 diabetes 5–10 years postpregnancy compared with women with normal glucose levels in pregnancy (Work Loss Data Institute, 2016 ). Furthermore, children from women with GDM have a higher likelihood of developing obesity and of having impaired glucose tolerance as well as diabetes, either in childhood or in early adulthood (World Health Organization [WHO], 2016 ).

Some risk factors that are identified for developing GDM include age (the risk for GDM increases with age), being overweight or obese, extreme weight gain during pregnancy and a family history of diabetes. Additional risk factors related to an increased frequency of GDM include GDM during an earlier pregnancy, a history of stillbirth or giving birth to an infant with congenital abnormalities and detection of glucose in the urine as well as ethnic background (Anna, van der Ploeg, Cheung, Huxley, & Bauman, 2008 ; Evensen, 2012 ; Kampmann et al., 2015 ; Khan, Ali, & Khan, 2013 ).

Early screening and diagnosis of GDM is therefore important to prevent or reduce complications during and postpregnancy for both mother and child. Most countries use selective screening, based on the known risk factors. Although selective screening could miss GDM cases, it could also assist nursing management by focussing health resources on women with the highest risk of complications, specifically in contexts where resources are scarce. Likewise, screening early in pregnancy for pre‐existent diabetes by determining fasting glucose is justified, especially in the context of increased existence of diabetes mellitus type 2 in young women, which often remains undiagnosed (Kampmann et al., 2015 ).

Once women are diagnosed with GDM, management includes lifestyle modifications in terms of a diet high in dietary fibre (specifically fruit and cereal) and with a low glycaemic index, as well as routine monitoring of blood glucose levels during and postpregnancy. Additionally, if needed, the GDM is treated by means of insulin, metformin and glyburide to ensure the long‐term health of the pregnant woman and her baby (ADA, 2015 ; Poomalar, 2015 ).

A guideline, developed from rigorous evidence, would assist nurses and midwives in the screening, diagnosis and management of GDM. As they are often the first point of care for women, this is particularly important in contexts where medical care is scarce. Although some guidelines on the management of GDM exist, they are often designed for medical practitioners. No study was found that summarized best practice guidelines regarding the nursing management of GDM. This study therefore searched for, selected, appraised, extracted and synthesized data from existing available guidelines to guide the development of a best practice guideline for the nursing management of GDM.

An integrative literature review was conducted following a five‐step process adopted from Whittemore and Knafl ( 2005 ). The processes proceeded as follows: Step 1: Formulation of the review question; Step 2: Literature searching; Step 3: Critical appraisal of evidence; Step 4: Data extraction; and Step 5: Data analysis. The integrative literature review was conducted by the first author, under supervision of the second and third authors, both of whom are experienced in conducting integrative literature reviews. The study was part of a larger study that aimed to develop a best practice guideline for the nursing management of GDM during the ante‐, intra‐ and postnatal periods.

2.1. Formulation of the review question

The review question (Step 1) was formulated according to the PICO format. The elements of the question were as follows: P – Population = Women; I – Issue = nursing management of GDM (including screening, diagnosis and management); C – Context = nursing and health institutions; O – Outcome = to inform best practices on the nursing management of GDM. The review question was therefore formulated as follows: What existing evidence is available to inform best practices on the nursing management of women diagnosed with GDM?

2.2. Literature searching process

The literature searching process (Step 2) was conducted with the assistance of an experienced librarian in selecting the databases and keywords. Inclusion and exclusion criteria were established to guide the search and selection process.

2.2.1. Sources of literature

Databases were thoroughly searched using the following search engines: BioMed Central, EBSCOhost (CINAHL, ERIC, Health Source: Nursing/Academic Edition, MasterFILE Premier, MEDLINE), JSTOR, PUBMED CENTRAL, SAGE, ScienceDirect, Google Scholar, Scopus and Wiley Online Library. A manual search for guidelines was performed, using Google Scholar and Google, accessing organizations specialized in developing best practice guidelines. These included Canadian Practice Guidelines, National Guidelines Clearinghouse (NGC), National Institute for Health and Clinical Excellence (NICE), Guidelines International Network, Scottish Intercollegiate Guidelines Network (SIGN), New Zealand Guidelines Group, National Health and Medical Research Council, Registered Nurses’ Association of Ontario, American College of Obstetricians and Gynaecologists, American Diabetes Association and Health Service Executives. Grey literature, such as unpublished theses and dissertations, responding to the management of GDM were also considered.

2.2.2. Key words

With the assistance of an experienced librarian, the combination of key words “guideline*” and “evidence‐based practice” and “gestational diabetes mellitus” AND “nurs* manage* OR nurs* intervention*” and “pregnan*, antenatal, intra‐natal OR postnatal*” was used. The combination of keywords used was adapted per database, if necessary, to obtain all relevant guidelines.

2.2.3. Inclusion and exclusion criteria

Guidelines were included that focussed on the nursing management of GDM where any of the following aspects are addressed: early screening for GDM and its management, self‐monitoring of blood glucose levels, lifestyle modifications and/or insulin administration. Studies published in English were used as this is the language the authors are proficient in. Guidelines published between 2004–2018 were included, and the most updated version of guidelines was included. Guidelines focussing on the management of type 1 or type 2 diabetes mellitus only were excluded as were guidelines that did not consider the practices of nurses or midwives in GDM management.

2.2.4. Search and selection process

The search for appropriate guidelines was conducted in June 2018. All guidelines that fitted the criteria for the study were retrieved and selected for inclusion. Guidelines that did not meet the required criteria were excluded. The inclusion and exclusion criteria were applied by both the first author and the fourth author (who served as an independent reviewer). Consensus regarding the inclusion and exclusion of relevant articles was reached between the authors. The search and selection process of the included guidelines is illustrated in Figure ​ Figure1’s 1 ’s PRISMA flow chart (Moher, Liberati, Tetzlaff, Altman, & PRISMA Group, 2009 ).

PRISMA flow of studies through the review (adapted from Moher et al., 2009 )

Figure ​ Figure1 1 shows that 28 guidelines were found in the literature search and retained for full‐text review. Seven guidelines were excluded, based on the study criteria, and two duplicates were excluded. Nineteen guidelines fulfilled the review criteria and were included for critical appraisal.

2.3. Critical appraisal

The AGREE II instrument was used to critically appraise the guidelines (Step 3). AGREE II consists of 23 appraisal items organized within six domains, followed by two global rating items for an overall assessment. Each domain captures a specific aspect of guideline quality. All AGREE II items were rated on a 7‐point scale (1 – “Strongly disagree”, when no relevant information was given, to 7 – “Strongly agree”, when the quality of reporting was exceptional and the criterion was fully met) (Brouwers et al., 2010 ). The rating for each item was done depending on the completeness and quality of reporting.

The overall score allocated to each guideline appraised was expressed as a percentage of the maximum possible score of 161. Guidelines with a score of 60 per cent were included as they were considered to have more rigour than guidelines with a lower score. Similarly, they were considered to contribute more weight to the discussion and recommendations derived from the review. Consensus was reached between the two reviewers (the first and fourth author), as a result of which one of the nineteen guidelines was excluded owing to poor rigour. A total of 18 guidelines were included for data extraction (Figure ​ (Figure1 1 ).

2.4. Data extraction process

After critical appraisal, data were extracted from eighteen guidelines (Step 4). This process was completed by the first and fourth authors, working independently. Data extraction focused on material relating to early screening and diagnosis of GDM and the nursing management of GDM.

2.5. Data analysis process

Thematic data analysis was used to systematically synthesize the extracted data of each guideline and develop themes (Step 5) (Burls, 2009 ). Consensus was achieved between the authors on the themes.

2.6. Ethical statement

The study obtained ethics from the University's Faculty Postgraduate Studies Committee (ethics number: H14‐HEA‐NUR‐32). The authors adhered to principles of honesty and transparency in reporting the data. Consent was not obtained, since this study had no participants.

Data extracted from the eighteen guidelines resulted in two main themes. They are, in outline, as follows: 1. Early screening and diagnosis of GDM; and 2. Nursing management of GDM (during pregnancy, intra‐ and postpartum management) (Table ​ (Table1). 1 ). Table ​ Table1 1 shows that most guidelines mentioned the nursing management of GDM during pregnancy ( N  = 17), followed by early screening and diagnosis of GDM ( N  = 16) and postpartum nursing management of GDM ( N  = 14). Intrapartum nursing management of GDM was least mentioned by the guidelines ( N  = 7). Table ​ Table2 2 provides a summary of the main recommendations per guideline, which are further discussed below.

Themes per guideline

Main recommendations per guideline

3.1. Early screening and diagnosis of GDM

Guidelines encourage early screening of the pregnant woman for possible identification and diagnosis of GDM, which can only be achieved if pregnant women are screened during antenatal visits. Scottish Intercollegiate Guidelines Network [SIGN] ( 2017 ) mentions a programme that must be designed for all pregnant women for early detection and treatment of GDM. Once women are screened and the results of the blood glucose tests fall within levels that can be diagnosed as GDM, the woman is considered as having GDM.

The timing of screening differs in the various guidelines. Most guidelines agree that early screening must be done at 24–28 weeks of gestation (American Association of Clinical Endocrinologists & American College of Endocrinology [AACE/ACE], 2010 ; Blumer et al., 2013 ; Diabetes Australia/Royal Australian College of General Practitioners [RACGP], 2016 ; Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ; Queensland, 2015 ; International Federation of Gynaecology & Obstetrics [FIGO], 2015 ; United States Preventative Services Taskforce [USPSTF], 2014 ) (see Table ​ Table2). 2 ). However, some guidelines recommend this to be done as early as possible or in the first trimester (Diabetes Australia/RACGP, 2016 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Society for Endocrinology, Metabolism, & Diabetes of South Africa [SEMDSA], 2017 ; FIGO, 2015 ). This often includes women that are at risk for developing GDM and, if negative, screening is repeated at 24–28 weeks of gestation (Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ; FIGO, 2015 ). The International Diabetes Federation ( 2009 ) specifically recommends determination of the women's risk of developing GDM at the first antenatal visit.

The method of screening recommended also differs. Most guidelines recommend the 2‐hr 75 g oral glucose tolerance test (OGTT) to aid with the diagnosis of GDM, while some guidelines opt for other tests, including the 50 g glucose challenge (Diabetes Australia/RACGP, 2016 ; USPSTF, 2014 ), the 2‐step screening test (Permanente, 2018 ) and the HbA1c (Queensland, 2015 ; FIGO, 2015 ). However, the AACE/ACE ( 2010 ) advises against the use of the HbA1c as a screening method to diagnose GDM, while NICE ( 2015 ) does not encourage the use of other screening tests (including fasting plasma glucose, random blood glucose, HbA1c, glucose challenge tests or urinalysis for glucose) to determine the risk of a woman developing GDM. Although the 2‐hr 75 g OGTT is recommended in most guidelines, its blood glucose values to diagnose GDM differ slightly. While some (Blumer et al., 2013 ; Queensland, 2015 ; SIGN, 2017 ; SEMDSA, 2017 ; WHO, 2013 ) recommend a fasting plasma glucose of 5.1–6.9 mM, 1‐hr value of >10.0 mM and 2‐hr value 8.5–11.0, according to NICE ( 2015 ), fasting values are <5.6 mM and 2 hr 7.8mM.

Specific aspects needing consideration during early screening and diagnosis are identified by various guidelines. For example, Blumer et al. ( 2013 ) recommend that the 75g OGTT be done after at least eight (8) hours night fast but not more than fourteen (14) hours. They further recommend that the usual intake of carbohydrates by the pregnant woman should not be reduced on the days preceding the OGTT test and the pregnant woman must be seated throughout the procedure. The International Diabetes Federation ( 2009 ) recommends that women that are at high risk for developing GDM should be offered healthy lifestyle advice during their first visit when screening is done. FIGO ( 2015 ) is the only guideline that considers low‐ and high‐resource contexts in their recommendations. FIGO ( 2015 ) recommends the use of a plasma‐calibrated hand‐held glucometer with properly stored test strips to measure plasma glucose in primary care settings, particularly in low‐resource countries (where a close‐by laboratory or facilities for proper storage and transport of blood samples to a distant laboratory may not exist). Using a plasma‐calibrated hand‐held glucometer may be more convenient and reliable than test results from a laboratory done on inadequately handled and transported blood samples.

3.2. Nursing management of GDM

Nursing management of GDM is a theme that is consistently featured in the guidelines that were included in the review. GDM management includes glycaemic control and monitoring and lifestyle modifications (diet and physical activity/exercise). Recommendations included those that should be used during pregnancy and intra‐ and postpartum.

3.2.1. During pregnancy

Glycaemic control and monitoring during pregnancy must be done, for example, once a week and thereafter every 2–3 weeks until delivery (International Diabetes Federation, 2009 ), to keep blood glucose levels within acceptable ranges for pregnancy (AACE/ACE, 2010 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; Permanente, 2018 ; SIGN, 2017 ; USPSTF, 2014 ; WHO, 2013 ). This is especially so where the woman is commenced on insulin therapy (AACE/ACE, 2010 ). According to Blumer et al., ( 2013 ), AACE/ACE ( 2010 ), FIGO ( 2015 ), Diabetes Australia/RACGP ( 2016 ) and ADA ( 2018 ), acceptable ranges are fasting blood sugar <5.3 mM, 1 hr pre‐prandial <7.8 mM and 2 hr postprandial <6.7 mM. Women with GDM must be encouraged to do self‐monitoring of blood glucose (ADA, 2018 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; FIGO, 2015 ). FIGO ( 2015 ) recommends that self‐monitoring should be done at least daily (low‐resource settings) and up to 3–4 times a day (high‐resource settings).

As lifestyle moderations are the first line of treatment (ADA, 2018 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ; International Diabetes Federation, 2009 ; Ministry of Health Malaysia, 2017 ; Permanente, 2018 ; FIGO, 2015 ; USPSTF, 2014 ), pharmacological treatment should only be provided if lifestyle moderations are inadequate to keep blood glucose targets within acceptable levels after 1–2 weeks (International Diabetes Federation, 2009 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ; Permanente, 2018 ). The preferred pharmacological treatment is insulin (AACE/ACE, 2010 ; ADA, 2018 ; International Diabetes Federation, 2009 ; Permanente, 2018 ; SEMDSA, 2017 ; FIGO, 2015 ), while metformin and glyburide can be used as effective alternatives (AACE/ACE, 2010 ; SIGN, 2017 ; FIGO, 2015 ) if not contraindicated or unacceptable for the woman (NICE, 2015 ). However, metformin should be prescribed/continued under specialist supervision (SEMDSA, 2017 ) but is not approved in Australia (Diabetes Australia/RACGP, 2016 ).

Health education should be provided on GDM and glycaemic control, especially on recognizing the signs of hypoglycaemia and treatment of those signs. Women should be made aware of the implications of GDM for the woman and the foetus and of steps to achieve management of GDM. Family members should be taught how to use the glucometer, as well as the management principles and importance of long‐term follow‐up (Diabetes Coalition of California, 2012 ; NICE, 2015 ; Queensland, 2015 ; FIGO, 2015 ).

In terms of diet, it is recommended that pregnant women with GDM receive nutrition counselling (Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; SIGN, 2017 ; USPSTF, 2014 ), preferably from a dietician familiar with GDM (ADA, 2018 ; Diabetes Canada, 2018 ; NICE, 2015 ; Queensland, 2015 ; FIGO, 2015 ). The nurse or midwife must make it a point to involve all the necessary healthcare professionals (Queensland, 2015 ) and preferably those with expertise in GDM (International Diabetes Federation, 2009 ; SEMDSA, 2017 ). A healthy diet should be high in vegetables and protein (Permanente, 2018 ) and low in GI (International Diabetes Federation, 2009 ; NICE, 2015 ). The recommended diet should consist of a minimum intake of 1,600–1,800 kcal/day and carbohydrate intake limited to 35%–45% of total calories (Blumer et al., 2013 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ). Weight gain in the pregnant woman with GDM must also be checked according to her BMI (Ministry of Health Malaysia, 2017 ; Queensland, 2015 ; FIGO, 2015 ). The nurse or midwife must encourage the pregnant woman with GDM to stick to the diet or nutrition planned with the dietician and also to monitor her blood glucose levels as scheduled.

In terms of exercise, moderate exercise is recommended, such as a 30 min’ (at least 10‐min periods) (Queensland, 2015 ) walk after meals (Blumer et al., 2013 ; NICE, 2015 ) or 1 hr a day (Permanente, 2018 ). Education should also be given about armchair exercises (American College of Obstetrics & Gynaecology [ACOG], 2018a ).

To provide the best nursing management for GDM, a customized plan of care, especially for women at high risk, should be developed (NICE, 2015 ) that is individualized and culturally sensitive (International Diabetes Federation, 2009 ). This care plan could also include checks of blood pressure and dipstick urine protein every 1–2 weeks (resourced settings) or monthly (low‐resource settings; FIGO, 2015 ; International Diabetes Federation, 2009 ; Queensland, 2015 ) as well as an ultrasound between 30–32 weeks of gestation to estimate foetal weight (Queensland, 2015 ) or every four weeks from 28–36 weeks of gestation (Ministry of Health Malaysia, 2017 ).

3.2.2. Intrapartum

Although guidelines differ regarding the delivery time and mode, most agree with an elective induction of 38–40 weeks to reduce the risk for stillbirths (Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Permanente, 2018 ). A caesarean section around 40 weeks plus 6 days is recommended, but this should be done before that time for those with comorbidities or maternal or foetal complications (NICE, 2015 ; Queensland, 2015 ). The primary objective of the intrapartum nursing management of GDM is to maintain maternal euglycemia to prevent neonatal hypoglycaemia, which is caused by the hyperinsulinemia in the baby due to hyperglycaemia in the mother. Close monitoring of women with GDM during labour and delivery should therefore be done (ACOG, 2018a ; Diabetes Canada, 2018 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; Queensland, 2015 ; SEMDSA, 2017 ) at least once an hour (ACOG, 2018a ) or, according to NICE ( 2015 ), every thirty (30) minutes till delivery. Maternal blood glucose levels must be maintained between 4.0 mM–7.0 mM (Diabetes Canada, 2018 ; Diabetes Coalition of California, 2012 ; Ministry of Health Malaysia, 2017 ; NICE, 2015 ; SEMDSA, 2017 ). To achieve these blood glucose levels, the woman should be given enough glucose during labour to help her to cope with the high level of energy demands for labour and for delivery so as to prevent the woman from having hypoglycaemia (Diabetes Canada, 2018 ; NICE, 2015 ; SEMDSA, 2017 ). NICE ( 2015 ) recommends that, if the capillary plasma glucose is above 7 mM, intravenous dextrose and insulin infusion must be given during labour and delivery, although the guideline does not specify how much.

3.2.3. Postpartum

Postpartum nursing management of GDM constitutes a critical challenge when treating women with GDM. Various guidelines selected for synthesis focus on postpartum management. It is recommended blood glucose‐lowering medication should be lowered immediately after delivery (International Diabetes Federation, 2009 ; Blumer et al., 2013 ; FIGO, 2015 ; Queensland, 2015 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ). Although guidelines recommend postpartum blood glucose screening for early detection of diabetes mellitus, impaired glucose tolerance or impaired fasting glucose (ACOG, 2018a ), they differ on when this should be done. Most guidelines recommend 6 weeks when the woman comes for postnatal follow‐up (Ministry of Health Malaysia, 2017 ; NICE, 2015 ; SEMDSA, 2017 ) or between 6–12/13 weeks (Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; NICE, 2015 ; Queensland, 2015 ). Blumer et al. ( 2013 ) is the only guideline that recommends, besides the 6‐ to 12‐week screening, that blood glucose monitoring should also be done 24–72 hr after delivery. This is to rule out high blood glucose levels just after delivery.

Most guidelines prefer a follow‐up of screening varying between 1 year (NICE, 2015 ; Permanente, 2018 ; SEMDSA, 2017 ) and 3 years (ACOG, 2018a ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ). According to ADA ( 2018 ), risk factors should be considered when deciding the timeframe for follow‐up screening. According to Diabetes Canada ( 2018 ), emails and phone calls can be used to remind women for their follow‐up screening. The method of screening recommended also differs, although a 2‐hr 75 g OCTT seems to be the most frequently used, as recommended by nine ( N  = 9) guidelines. ACOG ( 2018a ) recommend that women with impaired glucose tolerance or with impaired fasting glucose must be referred as early as practicable for prevention therapy.

In addition, women with a history of GDM must be counselled on preventative lifestyle modifications to reduce the risk of type 2 diabetes (ACOG, 2018a ; ADA, 2018 ; Blumer et al., 2013 ; Diabetes Australia/RACGP, 2016 ; Diabetes Canada, 2018 ; NICE, 2015 ; Queensland, 2015 ; SIGN, 2017 ; FIGO, 2015 ) specifically regarding their diet, weight control and exercise requirements (SIGN, 2017 ). Referral to a dietician can be done (Diabetes Canada, 2018 ). According to NICE ( 2015 ) women should be educated specifically with regard to the signs and symptoms of hyperglycaemia. Education on the risk of developing GDM in subsequent pregnancies should be included as well as the benefits of optimizing postpartum and inter‐pregnancy weight (Queensland, 2015 ).

Various guidelines (American College of Obstetrics and Gynaecology [ACOG], 2018b ; International Diabetes Federation, 2009 ; Blumer et al., 2013 ; FIGO, 2015 ; Queensland, 2015 ; Diabetes Australia/RACGP, 2016 ; Ministry of Health Malaysia, 2017 ; Diabetes Canada, 2018 ) recommend that women with GDM should be encouraged to breastfeed their newborns immediately after delivery, thereby helping to prevent hypoglycaemia in the newborn. It is recommended that continuous breastfeeding should be done for at least 3–4 months postpartum (Diabetes Canada, 2018 ; SIGN, 2017 ) or longer (Ministry of Health Malaysia, 2017 ) as this helps to reduce childhood obesity, glucose intolerance and diabetes later in life. However, caution should be advised regarding maternal hypoglycaemia if breastfeeding (SEMDSA, 2017 ) and skilled lactation support is therefore recommended (Queensland, 2015 ; FIGO, 2015 ). Finally, extra attention is also required to detect early signs of genitourinary, uterine and surgical site infections (in the case of an episiotomy and caesarean delivery; FIGO, 2015 ).

4. DISCUSSION

4.1. comprehensiveness of the guidelines.

Several guidelines from a variety of healthcare organizations, associations or health departments were found that include aspects relevant to the nursing management of GDM. Not all guidelines focus on all aspects (namely glycaemic control, monitoring and treatment and lifestyle moderations, including diet and physical activity/exercise) and phases of the nursing management of GDM (during pregnancy, intrapartum as well as postpartum) as only 8 ( N  = 8) of the guidelines reviewed include all phases of the management of GDM (ACOG, 2018a ; Diabetes Canada, 2018 ; NICE, 2015 ; Permanente, 2018 ; Queensland, 2015 ; SEMDSA, 2017 ; FIGO, 2015 ). There were guidelines which cover some of the phases or the nursing management of GDM in general. For example, the SIGN ( 2017 ) guideline does not focus on the nursing management of GDM during labour and delivery but does provide general recommendations on what should be done during pregnancy and postdelivery. NGC ( 2013 ) also does not discuss intrapartum nursing management of GDM but gives recommendations on the testing and diagnosis of pregnant women.

Guidelines also differed in the level of descriptiveness employed. Guidelines that were generally more descriptive with their recommendations included those from Blumer et al. ( 2013 ), AACE/ACE ( 2010 ), FIGO ( 2015 ), NICE ( 2015 ), SEMDSA ( 2017 ) and Diabetes Canada ( 2018 ). Additionally, variances in best practices regarding screening and diagnosis as well as the nursing management of GDM were observed. It is thus recommended that existing guidelines should be scrutinized in respect of their level of descriptiveness, together with the latest best evidence and of the quality of the evidence used to develop the recommendations in the guidelines.

4.2. Quality of evidence

Not all guidelines reviewed included the level or grades of evidence used for each recommendation and various levels or grades were used. This is required to select a recommendation for implementation that fits the context best and will yield the best outcomes for both mother and child. For example, some of the guidelines included did not use a grading system for evidence or references when citing the recommendations (Diabetes Coalition of California, 2012 ; NGC, 2013 ; NICE, 2015 ; Permanente, 2018 ), while others did not use a grading system for the evidence included, but did use a variety of evidence when citing the recommendations (International Diabetes Federation, 2009 ; Queensland, 2015 ; SEMDSA, 2017 ; USPSTF, 2014 ). Other guidelines included grading systems for the evidence of which an A–D grading system was the most commonly used which was adapted from the American Diabetes Association ( 2018 ). Grade A refers to clear evidence from well‐conducted, generalizable RCTs, grade B includes supportive evidence from well‐conducted cohort studies, while grade C and grade D refers to supportive evidence from poorly controlled or uncontrolled studies as well as expert consensus or clinical experience, respectively. Some guidelines included a variety of evidence supporting the recommendations (grade A–D) (AACE/ACE, 2010 ; Diabetes Australia/RACG, 2016 ; WHO, 2013 ), with two guidelines mainly using grade A and B evidence (ADA, 2018 ; Blumer et al., 2013 ), another two guidelines mainly using grade B and C evidence (ACOG, 2018a ; SIGN, 2017 ) and a fifth guideline mainly using grade C and D evidence to support the recommendations (Diabetes Canada, 2018 ). FIGO ( 2015 ) used the 2019 grading system, including mostly moderate quality evidence (+++) and very low‐quality evidence (+), while the guideline by the Ministry of Health Malesia ( 2017 ) used a grading system from the United States/Canadian Preventive Services Task Force ( 2001 ) where level I (at least one properly conducted RCT) and level III (expert opinions) were mostly used to support the recommendations. Therefore, in this review it was impossible to make a valid statement for each recommendation that was based on evidence grades/levels. A systematic review is therefore recommended which extends beyond the AGREEII tool that was undertaken in this study to summarize the overall strength of evidence of each recommendation, such as the screening, diagnosis and nursing management of GDM during pregnancy, intrapartum and postpartum care and the overall quality of each particular guideline. Additionally, only two guidelines considered the input from the woman in the management of GDM (International Diabetes Federation, 2009 ; NICE, 2015 ). Any recommendation or care plan developed should be discussed with the woman diagnosed with GDM and her permission should be obtained to implement the recommended care practices.

4.3. Resources/Barriers

Only one guideline considered the context in terms of low/high resources (FIGO, 2015 ). The reality is that most low‐resource countries are unable to implement some of the recommendations, such as, for example, universal 75‐g OGTT or self‐monitoring every day (FIGO, 2015 ). The possible barriers to the implementation of the recommendations caused by a lack of resources were not addressed in most of the guidelines. For example, several barriers to maternal health related to GDM have been identified. These include the lack of trained healthcare professionals; high staff turnover; lack of standard protocols and diagnostic tools, consumables and equipment; inadequate levels of financing of health services and treatment; and lack of or poor referral systems, feedback mechanisms and follow‐up systems.

Further barriers relate to distance to health facility; perceptions of female body size and weight gain/loss related to pregnancy; practices related to a pregnant women's diet; societal negligence of women's health; lack of decision‐making power among women regarding their own health; the role of women in society and expectations that the pregnant woman move to her maternal home for delivery; and lack of adherence to recommended postpartum screening and low continued lifestyle modifications ( 2017 , & Stray‐Pederson, 2 2017 ; Nielsen, Courten, & Kapur, 2012 ; Nielsen, Kapur, Damm, Courten, & Bygbjerg, 2014 ). Additionally, a recent delivery experience, baby's health issues, personal and family adjustment to the new baby, a negative experience of medical care and services and concerns about postpartum and future health (as in, for example, fear of being informed that they have diabetes) were specifically related to the barriers to postpartum follow‐up care (Bennett et al., 2011 ).

The barriers cited should be considered when implementing the recommendations offered by the guidelines. Further, an integration of health services should be offered as well as communication between the different healthcare professionals is required. Integration of health services can be done when postpartum follow‐up of a mother can be combined with the child's vaccination and routine paediatric care.

4.4. Recommendations

Kaiser and Razurel ( 2013 ) examined the determinants of health behaviours during the postpartum period in GDM patients. They found that the women's physical activity and diet do not often meet the recommended health‐promoting actions. Risk perception, health beliefs, social support and self‐efficacy were the main factors that were identified as having an impact on the adoption of health behaviours. GDM clients are encouraged to engage in lifestyle modifications or healthy behaviours during the postpartum period. It is important, therefore, to identify the factors that may influence these clients to continue with healthy behaviours (Kaiser & Razurel, 2013 ).

Education of the woman diagnosed with GDM on the screening, and management (including preventative lifestyles) is imperative and will assist in addressing some of the above‐mentioned barriers. Education, as mentioned by most guidelines, should preferably be given by nurses and/or midwives to all pregnant women that are at risk or diagnosed with GDM. Furthermore, the healthcare professionals will need to be trained on pregnancy‐specific lifestyle modifications, treatment and screening for complications (International Diabetes Federation, 2009 ). Finally, it is particularly important for low‐resource settings that availability of trained healthcare professions, self‐monitoring equipment and insulin supply, and laboratory resources for clinical monitoring of glucose control and assessment of renal damage (International Diabetes Federation, 2009 ) should be prioritized in national budgets for health care.

No contextualized guideline on the nursing management of GDM is available for contexts where women with GDM deal with specific challenges such as factors related to the health system, or socioeconomic and cultural conditions that may impose barriers to the implementation of the best practice. It is therefore recommended that, prior to the implementation, a context analysis should be conducted to identify specific barriers to its implementation. This was confirmed by FIGO ( 2015 ) who mentioned that local decisions will be required to decide whether a selective or universal approach will be used for each individual patient. Additionally, further research of the barriers is required to develop contextualized guidelines considering the challenges some women and some health systems may have in accessing or providing adequate maternal health care. The developed contextualized guidelines could then be piloted. Piloting will be done to determine how the guidelines could have a positive effect on the nursing management of GDM while considering the input from the pregnant women as well as possible barriers or resource constraints towards its implementation.

4.5. Limitations

Some limitations of the study were observed. A comprehensive search of a variety of databases available to the authors was used with the assistance of an experienced librarian. However, limited databases were available, and some organizations/ developers of guidelines were not subscribed to so some guidelines may have been missed. Although the reviewer possessed wide experience in appraising the guidelines, more independent reviewers could have reduced possible bias in the selection process of the guidelines.

5. CONCLUSION

Data extracted from the eighteen guidelines resulted in two main themes: 1. Early screening and diagnosis of GDM; and 2. Nursing management of GDM (during pregnancy, intra‐ and postpartum management). Although a variety of guidelines on the management of GDM were found, guidelines were not always comprehensive, sometimes differed in recommended practices and did not consider barriers to the implementation of the recommendations.

6. RELEVANCE TO CLINICAL PRACTICE

This study provides a summary of best practices regarding the diagnosis, screening and nursing management of GDM. The findings can be used by nurse–midwives when conducting maternal and postpartum follow‐up care for women at risk or diagnosed with GDM. However, critically scrutinizing the guidelines in terms of the best evidence used in their development and feasibility of the implementation of the recommendations for its context is required. Additionally, education of women with GDM could assist in addressing any barriers such as certain harmful health beliefs, a lack of social support and self‐efficacy to provide the best maternal health care. Further research is recommended to determine the strength of evidence of each recommendation and the development and implementation of a contextual guideline on the management of GDM that considers possible barriers and resource constraints towards its implementation.

CONFLICT OF INTEREST

The authors have no conflicts of interest to disclose.

ACKNOWLEDGEMENTS

The authors would like to thank Vicki Igglesden for editing the manuscript.

Mensah GP, ten Ham‐Baloyi W, van Rooyen DRM, Jardien‐Baboo S. Guidelines for the nursing management of gestational diabetes mellitus: An integrative literature review . Nursing Open . 2020; 7 :78–90. 10.1002/nop2.324 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]

• American Association of Clinical Endocrinologists and American College of Endocrinology [AACE/ACE] (2015). Clinical practice guidelines for developing a diabetes mellitus comprehensive care plan . American Association of Clinical Endocrinologists; Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4959114/pdf/nihms-803042.pdf [ PMC free article ] [ PubMed ] [ Google Scholar ]
• American Diabetes Association [ADA] (2010). Diagnosis and classification of diabetes mellitus . Diabetes Care , 33 ( 1 ), 6269. [ Google Scholar ]
• American College of Obstetrics and Gynaecology [ACOG] (2018a). Gestational diabetes mellitus. ACOG Gestational diabetes resource overview . Retrieved from https://www.acog.org/Womens-Health/Gestational-Diabetes [ Google Scholar ]
• American College of Obstetrics and Gynaecology [ACOG] (2018b). ACOG practice bulletin no. 190: Gestational diabetes mellitus . Obstetrics Gynecology , 131 ( 2 ), e49–e64. https://orcid.org/10.1097/AOG.0000000000002501 [ PubMed ] [ Google Scholar ]
• American Diabetes Association (2018). Strategies for improving care. Sec. 1. In: Standards of medical care in diabetes . Diabetes Care , 36 ( Supplement 1 ):S6–S12. [ Google Scholar ]
• Anna, V. , van der Ploeg, H. P. , Cheung, N. W. , Huxley, R. R. , & Bauman, A. E. (2008). Socio‐demographic correlates of the increasing trend in prevalence of gestational diabetes mellitus in a large population of women between 1995 and 2005 . Diabetes Care , 31 ( 12 ), 2288–2293. 10.2337/dc08-1038 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Association and [ADA] (2015). Management of diabetes in pregnancy . Diabetes Care , 38 ( Supplement 1 ), S77–S79. 10.2337/dc15-S015 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Association and [ADA] (2018). Management of diabetes in pregnancy: Standards of medical care in diabetes . Diabetes Care , 41 ( Suppl. 1 ), S137–S143. [ PubMed ] [ Google Scholar ]
• Bennett, W. L. , Ennen, C. S. , Carrese, J. A. , Hill‐Briggs, F. , Levine, D. M. , Nicholson, W. K. , & Clark, J. M. (2011). Barriers to and facilitators of postpartum follow‐up care in women with recent gestational diabetes mellitus: A qualitative study . Journal of Women’s Health (Larchment) , 20 ( 2 ), 239–245. 10.1089/jwh.2010.2233 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Blumer, I. , Hadar, E. , Hadden, D. R. , Jovanovič, L. , Mestman, J. H. , Murad, M. H. , & Yogev, Y. (2013). Diabetes and pregnancy: An endocrine society clinical practice guideline . Journal of Clinical Endocrinology & Metabolism , 98 ( 11 ), 4227–4249. 10.1210/jc.2013-2465 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Brouwers, M. , Kho, M. E. , Browman, G. P. , Burgers, J. S. , Cluzeau, F. , Feder, G. , & AGREE Next Steps Consortium (2010). AGREE II: Advancing guideline development, reporting and evaluation in healthcare . Canadian Medical Association Journal , 182 , E839–842. 10.1503/cmaj.090449 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burls, A. (2009). What is critical appraisal? Evidence based medicine . Retrieved from http://www.bandolier.org.uk/painres/download/whatis/What_is_critical_appraisal.pdf [ Google Scholar ]
• Chen, C. , Xu, X. , & Yan, Y. (2018). Estimated global overweight and obesity burden in pregnant women based on panel data model . PLoS ONE , 13 ( 8 ), e0202183 10.1371/journal.pone.0202183 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Diabetes Australia/Royal Australian College of General Practitioners [RACGP] (2016). General practice management of type 2 diabetes . East Melbourne: RACGP. [ Google Scholar ]
• Diabetes Canada (2018). Clinical practice guidelines . Retrieved from http://guidelines.diabetes.ca/docs/CPG-2018-full-EN.pdf [ Google Scholar ]
• Diabetes Coalition of California (2012). Basic guidelines for diabetes care . Retrieved from https://www.diabetescoalitionofcalifornia.org/guidelines/ [ Google Scholar ]
• Evensen, A. E. (2012). Update on gestational diabetes mellitus . Primary Care , 9 ( 1 ), 8394 10.1016/j.pop.2011.11.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Grading of Recommendations, Assessment, Development and Evaluation (GRADE) Working Group (2019). Criteria for applying or using GRADE . Retrieved from http://www.gradeworkinggroup.org/index.htm [ Google Scholar ]
• Guariguata, L. , Linnenkamp, U. , Beagley, J. , Whiting, D. R. , & Cho, N. H. (2014). Global estimates of the prevalence of hyperglycaemia in pregnancy . Diabetes Research in Clinical Practice , 103 ( 2 ), 176–185. 10.1016/j.diabres.2013.11.003 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• International Diabetes Federation (2009). Pregnancy and diabetes booklet (p. 136). Brussels: International Diabetes Federation. [ Google Scholar ]
• Kaiser, B. , & Razurel, C. (2013). Determinants of postpartum physical activity, dietary habits and weight loss after gestational diabetes mellitus . Journal of Nursing Management , 21 ( 1 ), 5869. [ PubMed ] [ Google Scholar ]
• Kampmann, U. , Madsen, L. R. , Skajaa, G. O. , Iversen, D. S. , Moeller, N. , & Ovesen, P. (2015). Gestational diabetes: A clinical update . World Journal of Diabetes , 6 ( 8 ), 1065–1072. 10.4239/wjd.v6.i8.1065 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Khan, R. , Ali, K. , & Khan, Z. (2013). Socio‐demographic risk factors of gestational diabetes mellitus . Pakistan Journal of Medical Sciences , 29 ( 3 ), 843846 10.12669/pjms.293.3629 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ministry of Health Malaysia (2017). Clinical practice guidelines: Management of diabetes in pregnancy . Putrajaya, Malaysia: Malaysia Health Technology Assessment Section. [ Google Scholar ]
• Mitanchez, D. , Yzydorczyk, C. , & Simeoni, U. (2015). What neonatal complications should the paediatrician be aware of in case of maternal gestational diabetes? World Journal of Diabetes , 6 ( 5 ), 734–743. 10.4239/wjd.v6.i5.734 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Moher, D. , Liberati, A. , Tetzlaff, J. , & Altman, D. G. , & PRISMA Group (2009). Preferred reporting items for systematic reviews and meta‐analyses: The PRISMA statement . PLoS Medicine , 6 ( 7 ), e1000097 10.1371/journal.pmed.1000097 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mukona, D. , Munjanja, S. P. , Zvinavashe, M. , & Stray‐Pederson, B. (2017). Barriers of adherence and possible solutions to nonadherence to antidiabetic therapy in women with diabetes in pregnancy: Patients' perspective . Journal of Diabetes Research , 2017 , 1–10. 10.1155/2017/3578075 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• National Guideline Clearinghouse [NGC] (2013). Work Loss Data Institute. Diabetes (Type 1, 2 and gestational) . Retrieved from https://www.ahrq.gov/gam/index.html [ Google Scholar ]
• National Institute for Healthcare and Excellence [NICE] (2015). Diabetes in pregnancy: management from preconception to the postnatal period . Retrieved from https://www.nice.org.uk/guidance/ng3/resources/diabetes-in-pregnancy-management-from-preconception-to-the-postnatal-period-51038446021 [ PubMed ] [ Google Scholar ]
• Nielsen, K. K. , de Courten, M. , & Kapur, A. (2012). Health system and societal barriers for gestational diabetes mellitus (GDM) services ‐ lessons from World Diabetes Foundation supported GDM projects . BMC International Health Human Rights , 12 , 33 10.1186/1472-698X-12-332012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Nielsen, K. K. , Kapur, A. , Damm, P. , de Courten, M. , & Bygbjerg, I. C. (2014). From screening to postpartum follow‐up – the determinants and barriers for gestational diabetes mellitus (GDM) services: A systematic review . BMC Pregnancy Childbirth , 14 , 41 10.1186/1471-2393-14-41 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Permanente, K. (2018). Gestational diabetes screening and treatment guideline (p. 112). Washington: Kaiser Foundation Health Plan of Washington. [ Google Scholar ]
• Poomalar, G. K. (2015). Changing trends in management of gestational diabetes mellitus . World Journal of Diabetes , 6 ( 2 ), 284–295. 10.4239/wjd.v6.i2.284 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Queensland (2015). Gestational diabetes mellitus . Queensland: Queensland Government. [ Google Scholar ]
• Rafiq, W. , Hussain, S. Q. , Jan, M. , & Najar, B. A. (2015). Clinical and metabolic profile of neonates of diabetic mothers . International Journal of Contemporary Pediatricians , 2 ( 2 ), 114118 10.5455/2349-3291.ijcp20150510 [ CrossRef ] [ Google Scholar ]
• Scottish Intercollegiate Guidelines Network [SIGN] . (2017). Management of diabetes . Retrieved from http://www.sign.ac.uk/assets/qrg116.pdf [ Google Scholar ]
• Society for Endocrinology, Metabolism and Diabetes of South Africa [SEMDSA] . (2017). SEMDSA 2017 Guidelines for the management of type 2 diabetes mellitus . Journal of Endocrinology, Metabolism and Diabetes of South Africa , 22 ( 1 Supplement 1 ), S1S196. [ Google Scholar ]
• The International Federation of Gynecology and Obstetrics [FIGO] (2015). The International Federation of Gynecology and Obstetrics (FIGO) Initiative on gestational diabetes mellitus: A pragmatic guide for diagnosis, management and care . International Journal of Gynaecology and Obstetrician , 131 ( S3 ), S173–S211. [ PubMed ] [ Google Scholar ]
• United States Preventive Services Task Force [USPSTF] (2014). Recommendation statement USPSTT screening for gestational diabetes mellitus: U.S. Preventive Services Task Force Recommendation Statement . Annals International Medicine , 160 ( 6 ), 414422. [ PubMed ] [ Google Scholar ]
• United States/Canadian Preventive Services Task Force (2001). Screening for otitis media with effusion: Recommendation statement from the Canadian Task Force on Preventive Health Care . Canadian Medical Association Journal , 165 ( 8 ), 1092–1093. [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Whitemore, R. , & Knafl, K. (2005). The integrative review: Updated methodology . Journal of Advanced Nursing , 52 ( 5 ), 546–553. 10.1111/j.1365-2648.2005.03621.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Work Loss Data Institute (2016). Guideline summaries . Retrieved from https://www.guidelinecentral.com/shop/gestational-diabetes-pocket-guide/ [ Google Scholar ]
• World Health Organization [WHO] (2013). Diagnostic criteria and classification of hyperglycaemia first detected in pregnancy (p. 2013). Geneva, Switzerland: WHO. [ PubMed ] [ Google Scholar ]
• World Health Organization [WHO] (2016). Global report on diabetes . World Health Organisation; Retrieved from http://apps.who.int/iris/bitstream/handle/10665/204871/9789241565257_eng.pdf;jsessionxml:id=87B0BCA0CBD924FCD2E6C34E791C2FDF?sequence=1) [ Google Scholar ]
• Zhu, Y. , & Zhang, C. (2016). Prevalence of gestational diabetes and risk of progression to type 2 diabetes: A global perspective . Current Diabetes Report , 16 , 7 10.1007/s11892-015-0699-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]