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Gestational Diabetes Mellitus—Recent Literature Review

Robert modzelewski.

1 Endocrinology, Diabetology and Internal Medicine Clinic, Department of Internal Medicine, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland

Magdalena Maria Stefanowicz-Rutkowska

2 Department of Endocrinology, Diabetes and Isotope Therapy, Wroclaw Medical University, 50-367 Wroclaw, Poland

Wojciech Matuszewski

Elżbieta maria bandurska-stankiewicz.

Gestational diabetes mellitus (GDM), which is defined as a state of hyperglycemia that is first recognized during pregnancy, is currently the most common medical complication in pregnancy. GDM affects approximately 15% of pregnancies worldwide, accounting for approximately 18 million births annually. Mothers with GDM are at risk of developing gestational hypertension, pre-eclampsia and termination of pregnancy via Caesarean section. In addition, GDM increases the risk of complications, including cardiovascular disease, obesity and impaired carbohydrate metabolism, leading to the development of type 2 diabetes (T2DM) in both the mother and infant. The increase in the incidence of GDM also leads to a significant economic burden and deserves greater attention and awareness. A deeper understanding of the risk factors and pathogenesis becomes a necessity, with particular emphasis on the influence of SARS-CoV-2 and diagnostics, as well as an effective treatment, which may reduce perinatal and metabolic complications. The primary treatments for GDM are diet and increased exercise. Insulin, glibenclamide and metformin can be used to intensify the treatment. This paper provides an overview of the latest reports on the epidemiology, pathogenesis, diagnosis and treatment of GDM based on the literature.

1. Introduction

Gestational diabetes mellitus (GDM) is a state of hyperglycemia (fasting plasma glucose ≥ 5.1 mmol/L, 1 h ≥ 10 mmol/L, 2 h ≥ 8.5 mmol/L during a 75 g oral glucose tolerance test according to IADPSG/WHO criteria) that is first diagnosed during pregnancy [ 1 ]. GDM is one of the most common medical complications of pregnancy, and its inadequate treatment can lead to serious adverse health effects for the mother and child [ 1 , 2 ]. According to the latest estimates of the International Diabetes Federation (IDF), GDM affects approximately 14.0% (95% confidence interval: 13.97–14.04%) of pregnancies worldwide, representing approximately 20 million births annually [ 3 ]. Mothers with GDM are at risk of developing gestational hypertension, pre-eclampsia and termination of pregnancy via Caesarean section [ 4 ]. In addition, GDM increases the risk of complications, including cardiovascular disease, obesity, and impaired carbohydrate metabolism, leading to the development of type 2 diabetes (T2DM) in both mother and infant [ 5 , 6 , 7 ]. The increase in the incidence of GDM also leads to a significant economic burden and deserves greater attention and awareness [ 8 ].

Despite numerous studies, the pathogenesis of GDM remains unclear, and the results obtained so far indicate a complex mechanism of interaction of many genetic, metabolic and environmental factors [ 9 ]. The basic methods of treating GDM include an appropriate diet and increased physical activity, and when these are inadequate, pharmacotherapy, usually insulin therapy, is used. In developing countries, such as Brazil, oral hypoglycemic agents are also used, mainly metformin and glibenclamide (glyburide) [ 10 ]. The prevention and appropriate treatment of GDM are needed to reduce the morbidity, complications and economic effects of GDM that affect society, households and individuals. Though it is well established that the diagnosis of even mild GDM and treatment with lifestyle recommendations and insulin improves pregnancy outcomes, it is controversial as to which type and regimen of insulin are optimal, and whether oral agents can be used safely and effectively to control glucose levels.

2. Aim of the Study

A review of current literature reports on epidemiology, pathogenesis, diagnosis and treatment of GDM.

3. Material and Methods

The study presents an analysis of data that are currently available in the literature that concern the epidemiology, pathogenesis, diagnosis and treatment of GDM. The study was based on reviews, original articles and meta-analyses published in English in the last 10 years.

A literature search was conducted from 1 January 2021 to 31 March 2022 using Web of Science, PubMed, EMBASE, Cochrane, Open Grey and Grey Literature Report. MeSH terms, including “gestational diabetes”, “pregnancy induced diabetes”, “hyperglycemia”, “glucose intolerance”, “insulin resistance”, ”prevalence”, “incidence”, “GDM treatment” and “behavioral treatment”, were used alone or in combination.

4. Results and Discussion

4.1. epidemiology.

The growing problem of overweight and obesity around the world significantly contributes to the steady increase in the incidence of diabetes, including GDM in the population of women of reproductive age [ 11 ]. According to the 2019 report by the International Diabetes Federation (IDF), more than approximately 20.4 million women (14.0% of pregnancies) presented with disorders of carbohydrate metabolism, of which approximately 80% was GDM, i.e., about one in six births was affected by gestational diabetes [ 3 ]. Table 1 presents the analysis of the geographical distribution of GDM [ 3 , 12 ].

The geographical distribution of GDM [ 3 , 12 ].

4.2. GDM Risk Factors

The incidence of hyperglycemia in pregnancy increases with age. According to Mosses et al., GDM was diagnosed in 6.7% of pregnancies in general, but in 8.5% of women over 30 years of age [ 13 ]. Lao et al. showed the highest risk of developing GDM at the ages of 35–39 compared with younger pregnant women (OR 95% CI: 10.85 (7.72–15.25) vs. 2.59 (1.84–3.67)) [ 14 ]. These observations were confirmed by IDF data showing the highest percentage of pregnancies with GDM reaching 37% at the ages of 45–49, which was also conditioned by a lower number of pregnancies with an accompanying general higher percentage of diabetes in this population [ 3 ]. The delivery of a macrosomic child is another important factor that may increase the risk of both GDM and DM2 by up to 20% [ 15 ]. Even after taking into account the age of the woman, pluriparity remains in a linear relationship to the incidence of GDM [ 16 ]. GDM in a previous pregnancy increases the risk of reoccurrence by more than six times [ 17 ]. In women with a BMI of at least 30 kg/m 2 , the GDM frequency is 12.3%, and in women with first-line relatives that have a history of GDM, it is 11.6%. The combination of these two factors increases the risk of GDM up to 61% of cases [ 4 , 18 , 19 ]. More than twice the percentage of pregnancies with GDM was observed in women that were previously treated for polycystic ovary syndrome (PCOS) [ 20 ]. Recent studies indicated that the prevalence of GDM is related to the season and that GDM prevalence increases during the summer compared with winter [ 21 , 22 , 23 ]. Moreover, a 50% increase in the incidence of GDM in pregnancies resulting from in vitro fertilization was described [ 24 ].

4.3. Diagnosing GDM

The decades-long polemic about the diagnosis of GDM has covered two issues: whether to include all pregnant women or only those with risk factors, and whether to use one- or two-stage diagnostic procedures. A GDM diagnosis is only possible if a previous diagnosis of diabetes (i.e., type 1 or type 2 diabetes) had been excluded early in the pregnancy. Screening of only risk groups may result in GDM not being diagnosed in as many as 35–47% of pregnant women, which is certain to affect obstetric results [ 25 ]. The results of the Hyperglycemia Adverse Pregnancy Outcome (HAPO) study of 23,316 women gave a clear outcome that elevated glycemia (but below the threshold for overt diabetes mellitus) showed a linear relationship with the occurrence of maternal and neonatal complications expressed as large for gestational age (LGA) endpoints, the frequency of Caesarean sections, neonatal hypoglycemia and the concentration of the umbilical C-peptide [ 26 ]. The current criteria for the diagnosis of GDM introduced by The International Association of Diabetes and Pregnancy Study Groups (IADPSG), which were based on the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) results, found a threefold increase in GDM diagnoses, which suggests an earlier underestimation. The HAPO group sought to identify new screening values that would better identify pregnancies at risk for perinatal complications. The HAPO study demonstrated a positive linear relationship between screening glucose values and adverse perinatal outcomes. Moreover, the study authors found that perinatal risks began to increase in women with glucose values that were previously considered “normal” [ 27 , 28 ]. Therefore, nowadays, the basis of GDM diagnostics is the administration of 75 g of glucose between 24 and 28 weeks of pregnancy in all pregnant women without previously diagnosed diabetes. The treatment of even mild forms of glucose intolerance in GDM offers an added benefit, as demonstrated by the Australian Carbohydrate Intolerance Study in Pregnant Women (ACHOIS) and Maternal-Fetal Medicine Units Network (MFMU). It was shown that the frequency of obstetric complications is reduced depending on hyperglycemia and pregnancy weight gain. In the ACHOIS study, the composite endpoint (neonatal death, perinatal injury, hyperbilirubinemia, neonatal hypoglycemia and hyperinsulinemia) was significantly reduced with antihyperglycemic intervention, and there was also a lower weight gain (by 1.7 kg on average) and a lower incidence of LGA. In the MFMU study, no changes were noted in the composite endpoint, but the incidence of LGA and shoulder dystocia decreased significantly [ 2 , 29 , 30 ]. The results of these studies showed that most scientific societies implement the recommendations of the IADPSG from 2010 and WHO from 2013 into their daily practice. The introduction of the IADPSG criteria for the screening of GDM increased the prevalence by threefold, albeit with no substantial improvements in GDM-related events for women without risk factors except for reduced risks for LGA, neonatal hypoglycemia and preterm birth [ 31 ]. This led to further research on a group of patients with GDM. In a large randomized trial (among 23,792 pregnant women), Hillier et al. showed that one-step screening, as compared with two-step screening, doubled the incidence of the diagnosis of GDM, but did not affect the risks of LGA, adverse perinatal outcomes, primary Caesarean section, or gestational hypertension or pre-eclampsia [ 32 ]. The GEMS Trial assessed two diagnostic thresholds for GDM—namely, the currently used, higher diagnostic criteria and the IADPSG, lower diagnostic criteria—for their effects on fetal growth, perinatal morbidity, maternal physical and psychological morbidity, and health service utilization. The recently published results of the GEMS Trial showed that lower glycemic criteria (fasting plasma glucose level of at least 92 mg/dL, a 1 h level of at least 180 mg/dL or a 2 h level of at least 153 mg/dL) for the diagnosis of GDM did not result in a lower risk of a large-for-gestational-age infant than the use of higher glycemic criteria (fasting plasma glucose level of at least 99 mg/dL or a 2 h level of at least 162 mg/dL) [ 33 ]. This latest study is another important point in the discussion of the best diagnosis method for GDM. Table 2 presents the criteria for the diagnosis of GDM according to different scientific societies.

The criteria for the diagnosis of GDM according to different scientific societies.

Notes: ADA—American Diabetes Association, ACOG—American College of Obstetricians and Gynecologists, DCCPG—Diabetes Canada Clinical Practice Guidelines, DIPSI—Diabetes in Pregnancy Society Group India, EASD—European Association for the Study of Diabetes, FIGO—International Federation of Gynecology and Obstetrics, ADIPS—Australasian Diabetes in Pregnancy Society, WHO—World Health Organization, IADPSG—International Association of the Diabetes and Pregnancy Study Groups, NICE—National Institute for Health and Care Excellence. 1 There are no established criteria for the diagnosis of diabetes mellitus in pregnancy based on a 1 h post-load value. 2 Refers to the whole blood glucose level. 3 Recommends either the IADPSG one-step or two-step approach; initial screening by measuring plasma or serum glucose concentration 1 h after a 50 g oral glucose load (GCT). Those exceeding the cut-off perform either a 100 g OGTT or 75 g OGTT, requiring two or more venous plasma concentrations to be met or exceed the threshold. 4 Listed in the preferred approach, the alternate approach is the IADPSG, which uses a non-fasting 75 g OGTT. 5 Uses a non-fasting 75 g OGTT.

Many potential markers of GDM occurrence are being described more and more frequently. The greatest hopes are connected with afamine, adiponectin and 1,5-anhydroglucitol [ 34 , 35 ]. Due to the fact that in many countries, prenatal care is provided by gynecologists who can consult other specialists, it seems important to develop predictive models that allow for the identification of women at the highest risk for gestational diabetes in early pregnancy. The Benhalim-2 2020 model, which takes into account interview and biochemical data (propensity score model: history of GDM, FPG, height, triglycerides, age, ethnic origin, first trimester weight, family history of diabetes, HbA1c), showed the highest sensitivity [ 36 ].

4.4. Pathogenesis of Carbohydrate Metabolism Disorders in Pregnancy

Several factors may be responsible for the occurrence of GDM, the most important of which are insulin resistance and beta cell dysfunction, as well as genetic, environmental and dietary factors.

4.4.1. Insulin Resistance

In the pathogenesis of GDM, as in type 2 diabetes, a key role is played by insulin resistance and decreased insulin secretion relative to the patient’s needs. We observe GDM in both obese and lean women [ 37 ]. Insulin resistance induced by pregnancy overlaps with the pre-pregnancy insulin resistance that is already present in obese women, while in lean women, an impaired first phase of insulin secretion is also dominant [ 38 ]. Insulin resistance in pregnancy is predisposed by the diabetogenic effect of placental hormones (human placental lactogen (hPL), human placental growth hormone (hPGH), growth hormone (GH), adrenocorticotropic hormone (ACTH), prolactine (PRL), estrogens and gestagens), increased secretion of pro-inflammatory cytokines (tumor necrosis factor alpha (TNF-α), IL-6, resistin and C-reactive protein (CRP)), adiponectin deficiency, hyperleptinemia and central leptin resistance, impaired glucose transport in skeletal muscles, impaired insulin receptor signaling, and decreased expression and abnormal translocation of GLUT-4 to the cell membrane of adipocytes [ 39 , 40 , 41 ]. An increased secretion of insulin-antagonistic hormones (placental hormones, cortisol) during pregnancy results in an increased insulin resistance, which, at the end of the third trimester, reaches a value similar to full-blown type 2 diabetes [ 9 , 42 ]. Subclinical inflammation in pregnant women as a result of the synthesis of pro-inflammatory cytokines in the placenta and adipose tissue also leads to insulin resistance [ 43 , 44 ]. So far, the effects on the development of insulin resistance due to TNF-α, IL-6 and C-reactive protein have been best studied. Kirwan et al. stated that an increase in insulin resistance, which is characteristic of pregnancy, most strongly correlates with the increase in TNF-α concentration, considering that TNF-α as a marker of insulin resistance during pregnancy [ 45 ]. Furthermore, hyperleptinemia in the first weeks of pregnancy is a predictor of the development of gestational diabetes. According to Qui, the determination of the leptin concentration ≥ 31.0 ng/mL in the 13th week of pregnancy causes a 4.7-fold increase in the risk of GDM compared with the risk at the level of leptinemia of ≤14.3 ng/mL. For every 10 ng/mL increase in leptin concentration, the risk of GDM increases by 21% [ 46 ]. At the same time, GDM is characterized by elevated concentrations of leptin, which leads to hyperleptinemia [ 47 ]. However, pre-pregnancy BMI is a stronger predictor of leptinemia than GDM perse [ 48 ]. In women with gestational diabetes, the concentration of adiponectin is lower than in pregnant women without disturbances of carbohydrate metabolism, regardless of their pre-pregnancy BMI [ 49 ]. It was shown that a low adiponectin concentration in the first and second trimesters of pregnancy is a predictor of diabetes development in pregnancy [ 50 ]. In the Barbour study, a 1.5–2-fold increase in the level of the p85α PI-3-kinase regulatory subunit was found in both the muscle and adipose tissue of obese pregnant and pregnant GDM women compared to obese non-pregnant women. In women with GDM, a 62% increase in the phosphorylation activity of IRS-1 serine residues was found in striated muscle cells compared with the control group of pregnant women without GDM, which points to insulin resistance post-receptor mechanisms [ 43 ].

4.4.2. β-Cell Dysfunction

The analysis of insulin secretion disorders in GDM gives inconclusive results. The mechanisms of β-cell hypertrophy and proliferation, resulting in a 300% increase in insulin secretion in the first two trimesters of physiological pregnancy, is insufficient to explain GDM [ 9 , 39 ]. In the pathogenesis of GDM, we also observed the influence of autoimmune and genetic factors, such as the presence of anti-insulin and/or anti-insulin antibodies, which are at risk of developing DM1 and latent autoimmune diabetes in adults (LADA) [ 51 ]. In cross-sectional studies, the prevalence of mutations in the gene variants GCK, HNF1A, HNF4A, HNF1B and INS in maturity-onset diabetes of the young (MODY) was 0–5% [ 52 ]. Great hopes in the search for the genetic causes of GDM are associated with research on the single nucleotide polymorphism (SNP) related to the cyclin-dependent kinase 5 (CDK5) regulatory subunit associated protein1-like1 gene (CKDAL1). Their presence is associated with an impaired first phase of insulin secretion in DM2 and GDM and leads to a decrease in the mass of beta cells and impairment of their function, leading to GDM [ 53 , 54 ].

4.4.3. Other Factors

A study conducted in Spain showed that carriers of the gene rs7903146 T-allele who followed the Mediterranean diet in early pregnancy had a lower risk of developing GDM [ 55 ]. A growing body of research provides evidence of the importance of DNA methylation in the regulation of gene expression associated with metabolic disturbances in pregnant women and in the metabolic programming of the fetus in the setting of GDM-induced hyperglycemia [ 56 , 57 , 58 ]. In subcutaneous and visceral adipose tissue samples, the insulin receptor mRNA/protein expressions were significantly reduced in women with GDM ( p < 0.05) [ 56 ]. Mothers with GDM displayed a significantly increased global placental DNA methylation (3.22 ± 0.63 vs. 3.00 ± 0.46% (±SD), p = 0.013) [ 57 ]. Additional light was shed on the pathogenesis of GDM by studies on disorders of the placental proteome, where the placental proteome was altered in pregnant women affected by GDM with large-for-gestational-age (LGA), with at least 37 proteins being differentially expressed to a higher degree ( p < 0.05) as compared with those with GDM but without LGA [ 59 ]. In addition, Khosrowbeygi et al. showed that women with GDM had higher values of TNF-α (225.08 ± 27.35 vs. 115.68 ± 12.64 pg/mL, p < 0.001) and lower values of adiponectin (4.50 ± 0.38 vs. 6.37 ± 0.59 µg/mL, p = 0.003) and the adiponectin/TNF-α ratio (4.31 ± 0.05 vs. 4.80 ± 0.07, p < 0.001) than normal pregnant women. The ratio of adiponectin/TNF-α, which decrease significantly in GDM compared with normal pregnancy, might be an informative biomarker for the assessment of pregnant women at high risk of insulin resistance and dyslipidemia and for the diagnosis and therapeutic monitoring aims regarding GDM [ 60 ].

4.5. COVID-19 Pandemic and GDM

The second severe acute respiratory distress syndrome (SEA) coronavirus (SARS-CoV-2) causes an acute respiratory disease called coronavirus disease 2019 (COVID-19). There are limited data on the impact of SARS-CoV-2 infection on the onset and course of GDM. A living systematic review and meta-analysis of 435 studies reported the incidence of COVID-19 in pregnant women of approximately 10% (7–14%) [ 61 ]. The COVID-19 pandemic has caused organizational difficulties related to the correct diagnosis of GDM. In Anglo-Saxon countries, in order to minimize the risk of infection with SARS-CoV-2, replacement of the three-point OGTT was proposed and the assessment of fasting blood glucose and Hba1c were introduced. Postpartum screening postponement and the use of telemedicine were also offered [ 62 ]. However, simplifying the diagnosis of GDM in order to avoid the risk of COVID-19 infection was unfortunately associated with the risk of not diagnosing GDM by as much as 20–30%, which may affect obstetric outcomes [ 63 , 64 , 65 ]. This was confirmed by another study that showed that in the “COVID era”, diagnostics toward GDM cannot be abandoned and the procedures for its detection cannot be simplified [ 66 ]. The COVID-19 pandemic increased the incidence of GDM in 2020 compared with 2019 (13.5% vs. 9%, p = 0.01), especially in women in the first trimester of pregnancy. Experiencing lockdown during the first trimester of gestation increased the risk of GDM in these women by a factor of 2.29 ( p = 0.002) compared with women whose pregnancies occurred before and after lockdown [ 67 ]. This is undoubtedly influenced by the sedentary lifestyle of women during the pandemic and reduced physical activity, most often caused by the fear of leaving their homes due to COVID-19 [ 68 ]. The “lockdown effect” caused a marked deterioration in glycemic control, an increase in the percentage of HBA1c, and weight/BMI gain in patients with DM2 and GDM [ 69 , 70 ].

4.6. Treatment of Gestational Diabetes

Regarding women with GDM, due to the lack of randomized clinical trials, it is extremely difficult to propose an unambiguous and uniform model of management in order to achieve obstetric results similar to the population of healthy women. The treatment of GDM is based on consensus and expert opinion. Analyses of Cochrane Database Reviews showed the lack of unambiguous data on the correlation between the intensity of glycemic control and obstetric outcomes [ 71 ]. Based on a meta-analysis from 2014–2019, Mitanchez et al. indicated that the greatest impact on reducing the number of obstetric complications is achieved by combining dietary treatment with exercise [ 72 ].

4.6.1. Nutritional Treatment

Nutritional recommendations help women to achieve normoglycemia, optimal weight gain and proper development of the fetus, and the introduction of a pharmacological treatment does not release the mother from the obligation to follow the diet [ 73 ]. In GDM, it is necessary to develop an individual nutritional plan based on glycemic self-control, optimal weight gain based on pre-pregnancy BMI, and a calculation of energy requirements and macronutrient proportions, as well as taking into account the mother’s nutritional preferences, together with work, rest and exercise [ 73 ]. Chao et al. indicated better results when using individualized recommendations for a specific woman with GDM in contrast to general recommendations [ 74 ]. It is recommended to eat three main meals and 2–3 snacks a day, often with a snack around 9:30 pm to protect against nocturnal hypoglycemia and morning ketosis [ 6 ]. In a prospective observational study using the 24 h online diet and glycemic tool (“Myfood24 GDM”), better glycemic control was demonstrated with more frequent meals [ 75 ]. In women with GDM, carbohydrates are the most important macronutrient, and their high consumption can cause hyperglycemia. However, glucose is the main energy substrate of the placenta and fetus, and thus, is necessary for their proper growth and metabolism [ 76 ]. According to the ATA, the content of carbohydrates in the diet should constitute 40–50% of the energy requirement, not less than 180 g/day, and consist mainly of starchy foods with a low glycemic index (GI) [ 6 , 73 ]. The recommended dietary fiber intake is 25–28 g per day, which means a portion of about 600 g of fruit and vegetables per day with a minimum of 300 g of vegetables, whole grain bread, pasta and rice [ 73 , 77 , 78 ]. Protein should constitute about 30% of the caloric value, that is about 1.3 g/kg of b.w./d, with the minimum recommended daily intake of 71 g of protein [ 73 ]. Increased intakes of plant protein, lean meat and fish, and reduced intakes of red and processed meats are beneficial in the treatment of GDM and may improve insulin sensitivity [ 79 , 80 ]. A diet with a high fat content is contraindicated (20–30% of the caloric value is recommended, including < 10% saturated fat), as it leads to placental dysfunction and infant obesity, increased inflammation and oxidative stress, and impaired maternal muscle glucose uptake [ 80 , 81 , 82 ]. The consumption of saturated fat should be limited in favor of the consumption of the polyunsaturated fatty acids (PUFA) n-3 (linolenic acid) and n-6 (linoleic acid), which are the most important fatty acids for fetal growth and development. A total intake of n-3 in the amount of 2.7 g/day is considered safe during pregnancy [ 77 ], while additional fish oil supplementation gives inconclusive results [ 83 ]. The recommended weight gain in pregnancy amounts to on average 8–12 kg, depending on the initial body weight ( Table 3 ) [ 78 ].

Weight gain in relation to baseline body weight (BMI).

A weight gain of over 18 kg is associated with a twice higher risk of macrosomia [ 84 , 85 ]. Many studies show an increase in the need for vitamins and minerals in pregnancy, mainly folic acid, vitamin D and iron. All pregnant women are recommended to supplement daily with 400 µg of folic acid and 5.0 µg of vitamin D; additionally, depending on the dietary intake, 500–900 mg of calcium and 27–40 mg of iron are recommended [ 77 ]. The influence of gut microbiota on the development of GDM is interesting [ 86 ]. So far, it was shown that in women in the third trimester of pregnancy, GDM was associated with altered intestinal microflora [ 87 ]. However, in the conducted studies on the beneficial effects of probiotics in the prevention or treatment of GDM, the results are still inconclusive [ 88 , 89 , 90 , 91 ].

The main quality-oriented recommendations include the need to limit or eliminate processed products with a high content of salt, sugar and fats; avoiding unpasteurized milk, raw meat, alcohol and caffeine; and ensuring proper hydration of at least 2 L of water per day. In addition, the effect of the Dietary Approach to Stop Hypertension (DASH) diet on glycemic control was confirmed, and Sarathi et al. indicated that eating a high-protein diet based on soy products reduces insulin requirements in GDM patients [ 92 , 93 ]. Myoinositol (vitamin B8) supplementation or a diet rich in the MYO-INS isomer may improve glycemic control in GDM [ 94 , 95 ].

4.6.2. Exercise in GDM

In women with GDM, the quantitative and qualitative recommendations for exercise are ambiguous in terms of improving glycemic control [ 96 ]. Obstetric indications and contraindications should be followed. If there are no contraindications, the available observational studies indicate the safety of physical activity during pregnancy [ 97 ]. Activities that can be safely started and continued are walking, cycling, swimming, selected pilates and low-intensity fitness exercises. It is safe to continue with (but not initiate) the following after consulting with one’s obstetrician: yoga, running, tennis, badminton and strength exercises. Pregnant people should avoid contact sports, horse riding, surfing, skiing and diving. The analysis of Aune et al. showed a reduction in the risk of GDM by 38% (RR 0.62, 95% CI 0.41–0.94) in physically active women [ 98 ]. An intervention study in overweight patients by Nasiri-Amiri et al. showed a 24% reduction in the risk of GDM in women exercising no more than three times a week [ 99 ]. In women with normal body weight, increased physical activity, according to an analysis by Ming et al., resulted in a lower weight gain in pregnancy without affecting the child’s weight or the frequency of Caesarean sections and a 42% reduction in the risk of GDM (RR 0.58, 95% CI 0.37−0.90, p = 0.01) [ 100 ]. A meta-analysis by Harrison et al. of eight randomized trials showed a significant reduction in fasting and postprandial glucose levels in women with 20–30 min of activity 3–4 times a week [ 101 ].

4.6.3. Pharmacological Treatment

Patients who cannot achieve glycemic targets with a properly balanced diet and elimination of dietary errors should be treated pharmacologically [ 29 ]. Most studies indicate insulin therapy as the safest form of treatment, and OAD (orally administrated drugs) treatment should be introduced only in the case of the patient’s lack of consent to insulin therapy or its unavailability [ 102 ]. Insulin therapy is carried out in the model of functional intensive insulin therapy (FIIT) with the use of subcutaneous injections. The safety of human insulin use in pregnancy was demonstrated [ 103 ]. The safety of the use of aspart and detemir analogs was confirmed in randomized trials [ 104 , 105 , 106 ] and the safety of lispro and glargine analogs was shown in observational studies [ 107 ]; none of the studies showed the passage of insulin analogs across the placenta [ 108 , 109 ]. Currently, metformin and glibenclamide are used as oral medications. Metformin and glibenclamide (glyburide) cross the placenta but are unlikely to be teratogenic [ 110 , 111 ]. The metformin in gestational (MiG) diabetes trial was a landmark study; it was one of the largest randomized controlled trials, in which 751 women with GDM prospectively assessed a composite of neonatal complications as the primary outcome and secondary outcome of neonatal anthropometry at birth. It was concluded that metformin alone, or with supplemental insulin, was not associated with increased perinatal complications. This trial was the basis of many subsequent studies to assess the safety and efficacy of metformin use in GDM [ 112 ]. Some studies showed that the use of metformin during pregnancy is associated with higher body weight, more visceral and subcutaneous tissue, and higher blood glucose levels when the offspring is 9 years old [ 113 ]. The use of glibenclamide, despite its high effectiveness, may result in a higher percentage of intrauterine deaths and neonatal complications, such as hypoglycemia, macrosomia and FGR (fetal growth restriction) [ 114 ]. Although there is an increasing amount of evidence that supports the use of glyburide or metformin for GDM, the American Diabetes Association (ADA) and American College of Obstetricians and Gynecologists (ACOG) still recommend insulin as the primary medical treatment if the glycaemic treatment goals are not achieved with lifestyle intervention due to the lack of evidence regarding the long-term safety of the alternatives [ 115 ]. Sodium-glucose cotransporter-2 (SGLT2) inhibitors block the transporter located in the proximal tubule of kidneys that promotes renal tubular reabsorption of glucose, which causes a decrease in blood glucose levels due to an increase in renal glucose excretion. Among women with diabetes, UTI during pregnancy can be associated with pyelonephritis and sepsis and potential long-term effects on the neonate [ 116 ]. There were some adverse events noted in animal reproductive studies, including adverse effects on renal development when SGLT2 inhibitors were used in the second and third trimesters, although there are no human data available. The use of SGLT2 inhibitors during pregnancy is not recommended [ 110 ]. Recently, some studies reported the use of GLP-1 agents in GDM. GLP-1 agents, including dipeptidyl peptidase-4 (DPP-4) inhibitor and glucagon-like peptide-1 receptor agonist (GLP-1 Ra), enhance insulin secretion in pancreatic b-cell and showed many benefits in treating diabetes mellitus type 2 but are not a common choice for GDM [ 117 , 118 ]. In a systematic review that included 516 patients and investigated the use of GLP-1 agents in GDM (at different time points, including the second trimester of pregnancy and after delivery), Chen et al. showed that the use of GLP-1 agents to normalize blood glucose and can improve insulin resistance, as well as reduce the rate of developing postpartum diabetes compared with a placebo. This systematic review suggested that a dipeptidyl peptidase-4 inhibitor and glucagon-like peptide-1 receptor agonist may be beneficial to GDM patients but need rigorously designed clinical trials to demonstrate this. In particular, whether it can be used during pregnancy to improve pregnancy outcomes or better used to prevent developing diabetes after delivery should be investigated [ 119 ]. The data of a randomized controlled trial, namely, The Treatment of Booking Gestational Diabetes Mellitus (TOBOGM), compared pregnancy outcomes among women with booking GDM receiving immediate or deferred treatment can provide new insights into the diagnosis and treatment of GDM [ 120 ].

5. Conclusions

GDM is one of the most common complications of pregnancy and confers lifelong risks to both women and their children. Observational data demonstrated a linear association between maternal glycemic parameters and risks for adverse pregnancy and offspring outcomes. SARS-CoV-2 infection will undoubtedly affect the risk of GDM. Many doubts regarding the diagnostic criteria and treatment of GDM are still under discussion. Treatment with insulin is effective, but costs and patient experiences limit its use in clinical practice. The use of metformin as a first-line agent for GDM remains controversial due to its transplacental passage and limited long-term follow-up data. Further clinical trials are necessary to use other oral hypoglycemic agents to treat GDM. It is very important for patients with GDM to receive behavioral therapy and to closely cooperate with the doctor. Future work in the field should include studies of both clinical and implementation outcomes, examining strategies to improve the quality of care delivered to women with GDM. The screening and treatment for GDM early in pregnancy are very controversial due to the lack of data from large randomized controlled trials. There is an urgent need for well-designed research that can inform decisions on the best practice regarding gestational diabetes mellitus screening and diagnosis.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, R.M., M.M.S.-R. and E.M.B.-S.; methodology, R.M. and W.M.; formal analysis, E.M.B.-S.; investigation, R.M. and M.M.S.-R.; resources, R.M., M.M.S.-R. and W.M.; data curation, E.M.B.-S.; writing—original draft preparation, R.M. and M.M.S.-R.; writing—review and editing, E.M.B.-S.; visualization, R.M. and M.M.S.-R.; supervision, E.M.B.-S.; project administration, R.M. and W.M. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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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.

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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

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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.

Graphical Abstract

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.

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.

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.

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  • Open access
  • Published: 08 August 2022

A scoping review of gestational diabetes mellitus healthcare: experiences of care reported by pregnant women internationally

  • Sheila Pham 1 ,
  • Kate Churruca 1 ,
  • Louise A. Ellis 1 &
  • Jeffrey Braithwaite 1  

BMC Pregnancy and Childbirth volume  22 , Article number:  627 ( 2022 ) Cite this article

7 Citations

5 Altmetric

Metrics details

Gestational diabetes mellitus (GDM) is a condition associated with pregnancy that engenders additional healthcare demand. A growing body of research includes empirical studies focused on pregnant women’s GDM healthcare experiences. The aim of this scoping review is to map findings, highlight gaps and investigate the way research has been conducted into the healthcare experiences of women with GDM.

A systematic search of primary research using a number of databases was conducted in September 2021. Studies were included if they had an explicit aim of focusing on GDM and included direct reporting of participants’ experiences of healthcare. Key data from each study was extracted into a purposely-designed form and synthesised using descriptive statistics and thematic analysis.

Fifty-seven articles were included in the analysis. The majority of studies used qualitative methodology, and did not have an explicit theoretical orientation. Most studies were conducted in urban areas of high-income countries and recruitment and research was almost fully conducted in clinical and other healthcare settings. Women found inadequate information a key challenge, and support from healthcare providers a critical factor. Experiences of prescribed diet, medication and monitoring greatly varied across settings. Additional costs associated with managing GDM was cited as a problem in some studies. Overall, women reported significant mental distress in relation to their experience of GDM.

Conclusions

This scoping review draws together reported healthcare experiences of pregnant women with GDM from around the world. Commonalities and differences in the global patient experience of GDM healthcare are identified.

Peer Review reports

Gestational diabetes mellitus (GDM) is defined as any degree of hyperglycaemia recognised for the first time during pregnancy, including type 2 diabetes mellitus diagnosed during pregnancy as well as true GDM which develops in pregnancy [ 1 ]. GDM is associated with a number of adverse maternal and neonatal outcomes, including increased birth weight and increased cord-blood serum C-peptide levels [ 2 ], as well as greater risk of future diabetes [ 3 ].

The global incidence and health burden of GDM is increasing [ 4 ] and the cost of healthcare relating to GDM significant. In 2019, the International Diabetes Federation estimated the annual global diabetes-related health expenditure, which includes GDM, reached USD$760 billion [ 4 ]. In China, for example, the annual societal economic burden of GDM is estimated to be ¥19.36 billion ($5.59 billion USD) [ 5 ].

GDM is estimated to affect 7–10% of all pregnancies worldwide, though the absence of a universal gold standard for screening means it is difficult to achieve an accurate estimation of prevalence [ 6 ], and the prevalence of GDM varies considerably depending on the data source used [ 7 ]. In Australia, for example, between 2000 and 01 and 2017-18, the rate of diagnosis for GDM tripled from 5.2 to 16.1% (3); furthermore, in 2017-18, there were around 53,700 hospitalisations for a birth event where gestational diabetes was recorded as the principal and/or additional diagnosis [ 8 ]. Important risk factors for GDM include being overweight/obese, advanced maternal age and having a family history of diabetes mellitus (DM), with all these risk factors dependent on foreign-born racial/ethnic minority status [ 9 ]. However, primarily directing research to understanding risk factors does not necessarily lead to better pregnancy care, particularly where diabetes is concerned, and developing better interventions requires consideration of women’s beliefs, behaviours and social environments [ 10 ].

To date there have been numerous systematic and scoping reviews focused on women’s experiences of GDM, which provide a comprehensive overview of numerous issues. However, gaps remain. In 2014, Nielsen et al. [ 11 ] reviewed qualitative and quantitative studies to investigate determinants and barriers to women’s use of GDM healthcare services, finding that although most women expressed commitment to following health professional advice to manage GDM, compliance with treatment was challenging. Their review also noted that only four out of the 58 included studies were conducted in low-income countries. In their follow-up review, Nielsen et al. specifically focused on research from low and middle income countries (LMIC) to examine barriers and facilitators for implementing programs and services for hyperglycaemia in pregnancy in those settings [ 12 ] and identified a range of factors such as women reporting treatment is “expensive, troublesome and difficult to follow”.

In 2014, Costi et al. [ 13 ] reviewed 22 qualitative studies on women’s experiences of diabetes and diabetes management in pregnancy, including both pre-existing diabetes and GDM. From their synthesis of study findings, they concluded that health professionals need to take a more whole-person approach when treating women with GDM, and that prescribed regimes need to be more accommodating [ 13 ]. Another 2014 review by Parsons et al. [ 14 ] conducted a narrative meta-synthesis of qualitative studies. Their 16 included studies focused on the experiences of women with GDM, including healthcare support and information, but the focus of their meta-synthesis was focused on perceptions of diabetes risk and views on future diabetes prevention.

In a systematic review of qualitative and survey studies from 2015, Van Ryswyck et al. [ 15 ] included 42 studies and had similar findings to Parsons et al. [ 14 ], also emphasising their findings regarding the emotional responses of women who have experienced GDM. Specifically, Van Ryswyck et al. [ 15 ] identified that women’s experiences ran the gamut of emotions from “very positive to difficult and confusing”, with a clear preference for non-judgmental and positively focused care. Most recently, the 2020 systematic review of qualitative studies by He et al. [ 16 ] synthesised findings from 10 studies to argue that understanding the experiences of women with GDM can aid health care professionals to better understand those under their care and to develop more feasible interventions to reduce the risk of DM. A further systematic review of qualitative studies by Craig et al. [ 17 ] focused on women’s psychosocial experiences of GDM diagnosis, one important aspect of healthcare experience, highlighting future directions for research into the psychosocial benefits and harms of a GDM diagnosis.

There has been insufficient consideration of epistemological assumptions and other aspects of research design which may affect how such studies are framed, which participants are included, how data is collected and subsequently what findings are spotlighted. While women’s experiences of GDM healthcare are often broadly included in reviews, they are not often the exclusive focus with healthcare experiences folded into accounts of living with GDM [ 11 ], healthcare service implementation [ 12 ], diabetes and pregnancy [ 13 ], understanding of future risk [ 14 ] and seeking postpartum care after GDM [ 15 ].

To address this gap, the aim of this review was to map the literature, identify gaps in knowledge and investigate the ways research has been conducted into GDM healthcare experiences. The research questions were:

When, where and how has knowledge been produced about women’s experiences of GDM healthcare?

What findings have been reported about women’s experience of GDM healthcare?

A scoping review was selected as the most appropriate method given our multiple aims relate to mapping the field of GDM healthcare experiences [ 18 ]. The reporting of this scoping review was guided by an adaptation of the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) reporting guidelines [ 19 ].

Search strategy

The search strategy was designed in consultation with a research librarian. The following databases were used: Scopus, PubMed, CINAHL, Web of Science, MEDLINE, Embase and Joanna Briggs Institute EBP. These databases were searched on 27 September 2021 by the first author using the keywords and MESH terms outlined in Table  1 . No limits were set on publication date, study design or country of origin. The reference lists of included articles were also examined to identify other potential articles (i.e. snowballing).

Study selection

References were downloaded into Endnote before being exported into the online systematic review platform Rayyan [ 20 ]. Titles and abstracts were first screened against inclusion criteria by the first author and uncertainties about article inclusion were referred to the second and third authors for a decision. A second reviewer independently screened a subset (5%) of titles and abstracts of studies for eligibility to ensure inclusion criteria were consistently applied. Studies were included if they reported primary (empirical) research in the English-language in a published peer-reviewed journal. Studies had to have an explicit aim of focusing on GDM and include direct reporting of participants’ experiences of healthcare. The experience of healthcare is here understood as being the patient experience of care occurring in formal clinical settings, including interactions with providers and other aspects of care prescribed by healthcare professionals. Exclusion criteria were reviews of any kind, research that was not empirical (e.g. personal accounts) and conference abstracts.

Data extraction and synthesis

Data from studies including authors, year published, study design, setting, sample size, recruitment site, stated theoretical approach, data collection method, languages and findings, were extracted into a custom template developed in Microsoft Excel. Findings were further summarised through an iterative coding process and used to develop a series of categories that broadly captured women’s experiences of GDM healthcare.

Search results

A total of 2856 articles were identified as potentially relevant to the research question from database searches. After removing duplicates ( n  = 811) and excluding non-relevant studies by screening titles and abstracts ( n  = 2045) and identifying an additional study through snowballing ( n  = 1), 112 articles were examined for inclusion through a full text assessment. Of these, 57 articles were included in this review, with 55 studies being excluded with reasons for exclusion documented. Figure  1 outlines the process of data gathering and Additional file: Appendix 1 for summarised study characteristics.

figure 1

The process of data gathering

Publication dates

All of the included studies were published from 2005 onwards, except for one early study published in 1994 [ 21 ]. There has been an overall increase in the number of studies published each year to 2020 (see Fig.  2 ).

figure 2

Included studies published over time

Research settings

For the vast majority of studies ( n  = 55, 91%), recruitment of women with GDM was conducted via hospitals, clinics and healthcare providers, with one of these studies also conducting additional recruitment via workplaces [ 22 ]. Electronic databases were used in two studies for recruitment, with one study using a national diabetes database in Australia [ 23 ] and another using electronic health data in the United States [ 24 ]. Two studies which targeted Indigenous populations relied on pre-existing relationships; a Canadian study gained entry to an Indigenous population by building on pre-existing relationships with the Mi’kmaq communities [ 25 ] and an Australian study which focused on Aboriginal populations relied on existing research networks [ 26 ]. Only one study recruited completely outside clinical, healthcare and research settings using advertisements and community notices in targeted areas of Atlanta, Georgia in the United States [ 27 ].

A handful of studies ( n  = 5, 9%) were based in countries classified as low- and lower middle-income; there were no countries considered ‘least developed’ [ 28 ]. For the most part, included studies were concentrated in a relatively small number of high-income countries, with the top six countries for research on women’s experiences of GDM healthcare being Australia ( n  = 11), Canada ( n  = 8), Sweden ( n  = 7), the United States ( n  = 6), the United Kingdom ( n  = 4) and China ( n  = 4). The remaining studies were spread across a number of countries, largely one study per setting: Austria [ 29 ], Brazil [ 30 ], Denmark [ 31 ], Ghana [ 32 ], India [ 33 ], Indonesia [ 34 ], Iran [ 35 , 36 ], Malaysia [ 37 ], New Zealand [ 38 , 39 ], Norway [ 40 ], Singapore [ 41 ], South Africa [ 42 , 43 ], Vietnam [ 44 ], Zimbabwe [ 45 ] (see Fig.  3 ).

figure 3

Settings of included studies

Forty-eight of the studies (84%) were conducted with participants in urban areas and the remaining studies ( n  = 9) were conducted in regional and rural areas of Australia [ 26 , 46 ], Canada [ 25 , 47 , 48 , 49 ], China [ 50 ], Tamil Nadu in India [ 33 ], and the state of New York in the United States [ 51 ]. A number of studies were conducted by the same research team and published in multiple installments; these studies were conducted in Lund, Sweden (6 studies), southeastern China (4 studies) and Melbourne, Australia (4 studies).

Participants

The majority of studies specifically focused on women diagnosed with GDM as the sole target group, though two studies also interviewed comparative groups of women with different conditions such as DM [ 27 , 52 ]. Several studies targeted women as well as healthcare professionals, including nurses, clinicians, general practitioners, with data being compared between groups [ 26 , 27 , 32 , 36 , 41 , 46 , 47 , 53 , 54 ]. In one study it was noted how some participants had pre-existing medical conditions, such as hypertension and HIV, and that their co-morbidities directly contributed to their perspective on GDM [ 36 ].

Depending on the nature of the study design—whether qualitative, mixed methods or quantitative—the range of participants varied greatly, from a small number of interview and focus group participants ( n  = 8) [ 55 ] through to large datasets such as the open-ended responses on a cross-sectional survey ( n  = 393) [ 23 ]. While there was some stratification of participants based on individual factors, such as body mass index [ 56 ] as well as glycaemic targets set [ 38 ], the main categorisation made was often in relation to ethnicity in studies from countries such as Australia, Sweden and the United States, where the focus on ethnic differences was built into the design of studies. For example, this included directly comparing ethnic groups, such as Swedish-born versus African-born [ 57 ], or comparing groups of women by their ethnicity, namely Caucasian, Arabic and Chinese [ 58 ].

Study designs

The studies varied in how they understood, described and measured women’s experiences of GDM healthcare. Of the 57 included studies, 50 (88%) used qualitative study designs. Only four studies (7%) had quantitative designs and three (5%) employed mixed-methods [ 29 ]. The vast majority of studies ( n  = 49, 86%) were cross-sectional, with seven studies [ 21 , 51 , 56 , 59 , 60 , 61 , 62 ] interviewing the same women at multiple time points. In terms of methodologies used, all the qualitative studies featured various types of interviews and/or focus groups. These were largely conducted face-to-face or via telephone. Seven studies employed more than one qualitative method to collect data [ 36 , 43 , 47 , 55 , 63 , 64 , 65 ] and, in addition, three studies used mixed methods to collect data [ 29 , 41 , 46 ]. One study focused on First Nations women in Canada used a focused ethnographic approach [ 49 ], and another 2021 study focused on South Asian women in Australia using ethnography [ 54 ]. The quantitative studies comprised four survey studies using questionnaires [ 37 , 38 , 52 , 66 ].

Theoretical approaches

The majority of studies did not specify a theoretical approach ( n  = 31, 54%), and relied on general data analysis approaches such as thematic analysis. Where a theory was referred to, it was largely used as a guiding framework for study design and data collection, and data analysis where applicable (see Additional file: Appendix 1 ). The three most popular theoretical approaches were the Health Belief Model ( n  = 6), Grounded Theory ( n  = 3) and phenomenology ( n  = 8), with the last of these specifically including hermeneutic [ 67 ] and interpretative approaches [ 63 , 68 ]. Two of the studies that focused on Indigenous populations used culturally-sensitive qualitative methodologies designed to respect and recognise Indigenous worldviews, namely the Two-Eyed Seeing Approach [ 25 ] and the Kaupapa Māori methodology [ 39 ]. Another study [ 47 ] focused on an Indigenous population discussed qualitative research in general being the most “flexible and interpretive methodology” and how using open-ended interviewing creates a dialogue which recognises Indigenous oral traditions and knowledge.

Data collection

Studies varied in when they captured data during the pregnancy and postpartum periods. Where the focus of a study was specifically on healthcare, women’s experiences were often elicited by researchers directly; otherwise, healthcare experience was generally revealed in relation to broader questions within the research framing, such as looking at factors that influence migrant women’s management of GDM [ 69 , 70 ] or examining barriers and possible solutions to nonadherence to antidiabetic therapy [ 71 ].

Almost all studies were conducted in a primary language of the research team, with fluency in the primary language largely requisite for participation. However, there were 14 studies involving multicultural populations that allowed women to use their preferred language as research teams consisted of multilingual researchers, research assistants or interpreters (see Table 2 ).

Study findings on women with GDM experiences of healthcare

The findings from the 57 included studies were categorised into a number of salient aspects of formal healthcare experience, then further categorised as being positive and/or negative experiences depending on how participants’ self-reports were described and quoted by study authors. Where there was not an explicit reference to sentiment in the study, it has not been recorded in this review.

Mental distress

Mental distress included acute emotional reactions such as shock and stress, as well as ongoing psychological challenges in coping with GDM. The vast majority of included studies noted mental distress of some kind ( n  = 48, 84%), inferring that mental distress was inextricably part of women’s experiences of GDM and intertwined with healthcare experience.

Patient-provider interactions

From the moment diagnosis of GDM occurs, a cornerstone of women’s healthcare experience is interactions with providers, which differs depending on the model of care offered. ‘Interactions’ can be broadly defined as interpersonal encounters where communication occurs directly through conversations at consultations as well as group sessions, or interactions via other means such as text messages, emails and phone calls. Forty-four studies ( n  = 44, 77%) discussed patient-provider interactions in their findings; these were positive experiences ( n  = 9, 20%), negative experiences ( n  = 16, 36%), or ambivalent, being both positive and negative ( n  = 19, 43%). As an example of positive experience, one study reported “women were happy with the care provided in managing their GDM, acknowledging that the care was better than in their home country.” [ 62 ] In terms of negative experiences, women felt, for example, healthcare providers could be “preachy” [ 55 ] and discount their own expertise in their bodies [ 21 ]. One study [ 40 ] specifically examined the difference in women’s experiences with primary and secondary healthcare providers, and found that overall they received better care from the latter. More generally, the participants from one study emphasised the importance of a humanistic approach to care [ 76 ].

Treatment satisfaction

Treatment satisfaction was a measure reported in two quantitative studies [ 37 , 52 ], and the mixed-methods study [ 29 ]. The Diabetes Treatment Satisfaction Questionnaire (DTSQ) was used in two studies to measure satisfaction [ 29 , 37 ]. The study by Anderberg et al. [ 52 ] used its own purposely developed instrument and found 89% of women with GDM marked “satisfied”, 2% marked “neutral” and no one indicated dissatisfaction. In the study by Hussain et al. [ 37 ], which used the DTSQ, 122 (73.5%) patients reported they were satisfied with treatment and 44 (26.5%) were unsatisfied; overall, the majority of patients were satisfied with treatment but retained a ‘negative’ attitude towards GDM. The study by Trutnovsky et al. [ 29 ] went further in its analysis as women responded to the DTSQ at three different phases – before treatment, during early treatment and during late treatment – and found that overall treatment satisfaction was high, and significantly increased between early and late treatment.

Diet prescribed

Diet is a fundamental component of treatment for GDM. Once diagnosed, many women are prescribed modified diets to maintain blood sugar levels, which they record on paper or by using an electronic monitor at specified times. Thirty-nine studies ( n  = 39, 68%) included findings and discussion about women’s experiences of prescribed diet, and of those studies ( n  = 33, 84%) this is captured as generally a negative experience. In some studies, women’s experience of the prescribed diet was reported as being both positive and negative ( n  = 4, 10%); only one study ( n  = 1, 3%) recorded it as a positive experience [ 38 ]. The difficulty of following a new diet during pregnancy was a key reason as to why the experience was negative, as well as practical considerations such as being able to easily access fresh food in remote areas [ 26 ]. In studies with multicultural populations, negative experience related to managing the advice in conjunction with culturally-based diets. As noted in the two studies led by Bandyopadhyay, women had difficulty maintaining their traditional diet due to the new restrictions placed upon them [ 54 , 62 ].

Medication prescribed

Medication for GDM primarily involves some form of insulin, which is prescribed to manage blood sugar levels. Twenty-one studies ( n  = 21, 37%) included findings and discussion about women’s experiences of GDM medication and of those, it was mostly reported as being a negative experience ( n  = 13, 62%), with various reasons captured including insufficient time to “figure things out” [ 77 ] and causing feelings of anxiety and failure [ 78 ]. However, in a few studies prescribed medication was noted as being a positive experience ( n  = 3, 14%), or both a positive and negative experience ( n  = 5, 24%). In one study, a participant stated, “the fact that I’m on insulin makes it easy” [ 68 ].

Monitoring captures both the direct monitoring conducted by healthcare providers, primarily blood and blood sugar level tests as well as ultrasounds, as well as self-monitoring women were required to carry out and which was often then verified by healthcare professionals. Twenty studies ( n  = 20, 35%) included findings and discussion about women’s experiences of monitoring and of those it was seen as being negative ( n  = 14, n  = 70%), both positive and negative ( n  = 5, 25%) and positive ( n  = 1, n  = 5%). In the one study that reported positive experiences only, a participant reported that she thought it was good “they are monitoring us all the time” [ 30 ]. Studies reporting negative experiences with monitoring had participants citing reasons such as feeling over-scrutinised [ 65 ].

Access to timely healthcare

Access to healthcare can be a challenge in certain settings, and, even when access is possible, timeliness can be an issue. Of the 31 studies ( n  = 31, 54%) that referred to access in their findings, the vast majority of these studies ( n  = 28) reported access to timely healthcare being a negative experience, with reasons cited including geographic distance [ 39 , 46 ], difficulties in being able to make a booking to be seen at a hospital [ 79 ] and then, when being seen, not having enough time with a healthcare provider [ 27 , 44 ]. In one of the two studies reporting positive experiences [ 52 ], all questions relating to accessibility indicated satisfaction (97%); in the other of the two studies [ 38 ], the majority of women (68%) appreciated that health professionals took time to listen and explain.

Provision of information

Information to support women is critical in managing their GDM diagnosis. Ongoing management came from meetings with healthcare providers—described in one study as being “frontline support” [ 79 ]— alongside sources focused on diet, medication, exercise and other pertinent information. Across all the studies which discussed how provision of information by healthcare providers was received ( n  = 38, 67%), it was noted as largely negative ( n  = 24, 63%) and both positive and negative ( n  = 10, 18%), though there were discussions of positive experiences ( n  = 4, 7%). Considered together, all the studies suggested how crucial clear information is to a positive experience of healthcare. For women, having inadequate knowledge about how to cope was a source of disempowerment and, across the majority of studies ( n  = 44, 77%), participants reported they found information from providers was insufficient. Interestingly, one of these studies found the insufficiency was actually due to the information being “too much” [ 26 ], while another study [ 59 ] found there was a desire for “more frequent controls and dietary advice”. The inappropriate timing of information was also reported in a number of studies [ 31 , 58 , 79 , 80 , 81 ]. One study noted how participants found one group of healthcare providers, midwives and nurses provided better information than general practitioners [ 40 ], while another noted the contradictory nature of advice from different providers [ 82 ]. Language barriers were also identified as a problem with information provision with a lack of information available in a woman’s preferred language [ 69 ].

Financial issues

Direct healthcare costs including out-of-pocket medical consultation fees, medication and medical equipment were primarily raised by participants in the United States [ 27 ], Ghana [ 32 ] and Zimbabwe [ 45 ], with the last of these reporting that some participants discussed “the related costs of treatment … resulted in participants foregoing some of the tests and treatments ordered” [ 45 ]. A study from Canada noted a number of participants with refugee status discussed the “economic challenge” of managing GDM and that the cost of diabetes care “was quite high and difficult to manage” [ 83 ]. Several indirect costs were also discussed across the studies. In a number of studies ( n  = 7), the additional cost of purchasing healthy food to manage GDM was brought up as being a burden [ 25 , 27 , 38 , 42 , 48 , 51 , 84 ]. However, in one study, women said the costs related to food went down as being able to buy take-away (fast foods) became restricted [ 38 ]. Loss of income [ 46 ] as well as daycare costs were cited [ 25 ], as was additional transportation and hospital parking costs [ 39 , 46 , 56 ]. Finally, women in one study reported having to change occupations and even quit work to manage GDM [ 21 ].

The growing number of research studies relaying women’s GDM healthcare experience is encouraging, given increasing incidence and health burden. As this review demonstrates, there are important commonalities across all studies, suggesting that some aspects of GDM healthcare experience seem to be universal; mental distress, for example, was reported in most studies. In contrast, other aspects of GDM healthcare experience seem to relate to factors specific to local settings; financial issues were mainly raised in settings where healthcare is not universal or is not readily affordable. Related financial issues were raised by participants in a number of rural-based studies, revealing something of a difference between urban and rural healthcare settings regardless of country context.

All of the included studies relied on women’s self-reporting without necessarily involving other measures, which broadly fell into two categories: women currently undergoing care for GDM at the time of study data collection and those looking back on past experience. Included studies were overwhelmingly qualitative in design, with relatively small numbers of participants for each category; put together, though, they paint a broad picture of women’s GDM healthcare experience across a range of settings. As the phenomenon being examined here is women’s experiences, qualitative methodologies are vital given the experience of health, illness and medical intervention cannot be quantified [ 85 ]. On the other hand, quantitative studies are able to include far more participants, though it is important to note not necessarily greater applicability and generalisability; when both types of studies are considered together as in mixed-methods study designs, there is a possibility of corroboration, elaboration, complementarity and even contradiction [ 85 ].

Recruiting women through clinical and other healthcare settings, as almost all of the included studies did, necessarily leads to biased samples of participants likely to be ‘compliant’ with healthcare requirements and treatment regimens. As one study noted, compliance was high despite limited understanding of GDM and dietary requirements, as well as why change was required [ 71 ]. This scenario occurs against the backdrop of the inherent power imbalance which exists in patient-provider relationships in reproductive healthcare [ 86 ]. A few of the included studies demonstrated reflexivity for this issue, with the studies most sensitive to these concerns focused on Indigenous populations. This power imbalance also exists in patient-researcher relationships [ 87 ]; a critical way to mitigate this effect is to actively include participants in research design, which only one included study reported doing 75]. This suggests an important direction for future studies, building on recent work involving patients to establish research priorities for GDM [ 88 ]. Indeed, many of the included studies did incorporate ideas about improving healthcare as proposed by the women themselves. For example, in one study, participants reported that small group sessions with medical practitioners and more detailed leaflets would be useful [ 44 ], suggesting how current sessions could be run better.

Culturally sensitive qualitative methodologies were employed with Indigenous populations and those learnings could be further extended to other groups of research participants. GDM is known to be more common in foreign-born racial minorities [ 9 ], so it is encouraging that some studies focused on these particular groups and had study designs that included interpreters. However, this line of research is arguably under-developed given most studies excluded minoritised women who did not have a high degree of fluency in the dominant language. Language barriers were identified as a problem with information provision with GDM healthcare [ 69 , 70 ], and it is possible to extend this idea to research contexts themselves. Not being able to use the language one feels most fluent in clearly affects the way GDM healthcare experiences are reported.

Treatment satisfaction was used in both quantitative and mixed-method studies, but as a solo measure the insights it can provide is limited; we do not exactly know why or how, for example, women’s satisfaction improves later in GDM care [ 29 ]. However, a number of the studies provide possible answers. Persson et al. [ 61 ] describe the process women underwent “from stun to gradual balance” due to a process of adaptation that became easier “with increasing knowledge” about how to self-manage GDM. Ge et al. [ 89 ] found that women developed a philosophical attitude over time to reach a state of acceptance, and such a shift in attitude would clearly have an impact on how healthcare is received and understood. These findings suggest the benefit of both time and experience, and the role of these factors could be better examined with more longitudinal studies.

In this scoping review, under half of the included studies explicitly drew on theory. But as argued by Mitchell and Cody [ 90 ], regardless of whether it is acknowledged, theoretical interpretation occurs in qualitative research. Explicitly incorporating theoretical approaches are valuable in strengthening research design when such conceptual thinking clearly informs the research process; here, examining women’s lived experiences without articulating the theoretical bases which underpins research design and analysis leads to a lack of acknowledgement of relevant context as to how both treatment and research occurs. For example, gender exerts a significant influence upon help-seeking and healthcare delivery [ 91 ], and particularly for GDM. In future, it might be useful to further consider the value of theory in elucidating women’s experiences to address biases in research design to further the fields of study which relate to women’s GDM experiences [ 90 ].

Finally, much of this research has been generated in a small number of wealthy countries. GDM is a growing problem in low income settings and yet, as Nielsen et al. [ 92 ] describe, detection and treatment of GDM is hindered due to “barriers within the health system and society”. Going further, Goldenberg et al. suggest that due to competing concerns, “diagnosing and providing care to women with diabetes in pregnancy is not high on the priority lists in many LMIC”. [ 93 ] Similar barriers exist with GDM research endeavours; ensuring that evaluation of healthcare includes women’s experiences of GDM healthcare would be valuable to researchers in these settings and beyond. Thus there are clear gaps in practice as well as the research literature in considering women’s experiences of GDM healthcare internationally.

Implications

Research into women’s experience of GDM healthcare continues to accumulate and continued research efforts will contribute to far greater understanding of how we might best support women and improve healthcare outcomes. However, there is room for improvement, such as by following participants longitudinally, using mixed methods and taking more reflexive and theoretically informed approaches to researching women’s experiences of GDM healthcare. There is a need highlighted for more culturally sensitive research techniques as well as including women in the study design process, and not just as research subjects to be instrumentalised for developing recommendations for clinical delivery.

Strengths and limitations

Secondary analyses of primary research are challenging to conduct when the pool of included studies is highly heterogeneous. In this scoping review, in order to synthesise a large group of diverse studies, summarising results in terms of positive and negative experiences of GDM healthcare was reductive but necessary. This key strength of our review, inspired by sentiment analysis [ 94 ], shows the utility in capturing overall polarity of feelings as it highlights the ambivalence of healthcare experience. An additional strength was involving a research librarian to help design the searches and advise on relevant databases.

There are several limitations. For our search strategy, we used a broad set of terms relating to patient experience, but there is no standard set of terminology about this type of research, so it is possible some studies were missed. Only studies in English were included, so any studies published in other languages were missed. We did not conduct a critical appraisal on the included studies, which was a limitation; however, this was a purposeful choice in order to include a wide range of studies, including from research settings that are not as well-resourced.

This scoping review identifies commonalities in how GDM healthcare is delivered and received in settings around the world, with women’s experiences varying depending on what model of care is applied alongside other factors. Documenting experiences of GDM healthcare is a vital way to inform future policy and research directions, such as more theoretically informed longitudinal and mixed method approaches, and co-designed studies.

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

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Acknowledgements

Jeremy Cullis, Clinical Librarian at Macquarie University, provided invaluable assistance with the database search strategy.

SP is being supported by a Macquarie Research Excellence Scholarship, funded by both Macquarie University and the Australian Government’s Research Training Program.

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SP led and executed the study with design support, input and advice from KC, LAE and JB. SP supported by KC assessed the literature. LAE provided statistical and methodological expertise alongside the other authors, and JB provided strategic advice. All authors reviewed and provided editorial suggestions on SP’s draft and agreed with the final submitted version. All authors read and approved the final manuscript.

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Characteristics of the studies included in the scoping review

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Pham, S., Churruca, K., Ellis, L.A. et al. A scoping review of gestational diabetes mellitus healthcare: experiences of care reported by pregnant women internationally. BMC Pregnancy Childbirth 22 , 627 (2022). https://doi.org/10.1186/s12884-022-04931-5

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  • http://orcid.org/0009-0006-9213-3135 Ines Hebeisen 1 , 2 ,
  • Elena Gonzalez Rodriguez 3 ,
  • http://orcid.org/0000-0002-8938-5095 Amar Arhab 1 , 2 ,
  • Justine Gross 4 ,
  • Sybille Schenk 5 ,
  • Leah Gilbert 1 ,
  • Katrien Benhalima 6 ,
  • Antje Horsch 7 , 8 ,
  • http://orcid.org/0000-0002-3091-9400 Dan Yedu Quansah 1 ,
  • Jardena J Puder 1 , 2
  • 1 Obstetric Service, Department Woman-Mother-Child , Lausanne University Hospital , Lausanne , Switzerland
  • 2 University of Lausanne , Lausanne , Switzerland
  • 3 Interdisciplinary Center of Bone Diseases, Bone and Joint Department , Lausanne University Hospital , Lausanne , Switzerland
  • 4 Service of Endocrinology, Diabetes and Metabolism, Department of Medicine , CHUV , Lausanne , Switzerland
  • 5 Service of Obsterics, Department Woman-Mother-Child , Lausanne University Hospital , Lausanne , Switzerland
  • 6 Department of Endocrinology , KUL UZ Gasthuisberg , Leuven , Belgium
  • 7 Neonatology service, Department Woman-Mother-Child , Lausanne University Hospital , Lausanne , Switzerland
  • 8 Institute of Higher Education and Research in Healthcare (IUFRS) , University of Lausanne , Lausanne , Switzerland
  • Correspondence to Professor Jardena J Puder; Jardena.puder{at}chuv.ch

Introduction The aim of the study is to investigate prospective associations between breastfeeding and metabolic outcomes, inflammation, and bone density in women with prior gestational diabetes mellitus (GDM).

Research design and methods We prospectively included 171 women with GDM from the MySweetheart trial. Women were followed during pregnancy (from 24 up to 32 weeks’ gestational age) up to 1 year postpartum. Outcomes included weight, weight retention, body composition, insulin resistance and secretion indices, C reactive protein (CRP), and bone density. We compared differences in the associations between breastfeeding and health outcomes between women who breast fed <6 months vs ≥6 months. Analyses were adjusted for potential medical and sociodemographic confounders.

Results Breastfeeding initiation was 94.2% (n=161) and mean breastfeeding duration was 6.6 months. Breastfeeding duration was independently associated with lower weight, weight retention, body fat, visceral adipose tissue, lean mass, CRP, insulin resistance (Homeostatic Model Assessment for Insulin Resistance), and insulin secretion (Homeostatic Model Assessment of β-cell index) at 1 year postpartum (all p≤0.04) after adjusting for confounders. Breastfeeding was associated with higher insulin resistance-adjusted insulin secretion (Insulin Secretion-Sensitivity Index-2) in the unadjusted analyses only. There was no association between breastfeeding duration and bone density. Compared with <6 months, breastfeeding duration ≥6 months was associated with lower weight, weight retention, body fat, fat-free mass as well as lower CRP at 1 year postpartum (all p<0.05) after adjusting for confounders.

Conclusions Longer breastfeeding duration among women with prior GDM was associated with lower insulin resistance, weight, weight retention, body fat and inflammation, but not lower bone density at 1 year postpartum. Breastfeeding for ≥6 months after GDM can help to improve cardiometabolic health outcomes 1 year after delivery.

  • Bone Density
  • Body Weight

Data availability statement

Data are available upon reasonable request. Not applicable.

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/ .

https://doi.org/10.1136/bmjdrc-2024-004117

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Breastfeeding is a modifiable risk factor for cardiometabolic diseases; however, there is no consensus on a recommended breastfeeding duration to improve maternal health.

There are limited data on the relationship between breastfeeding and body composition, insulin resistance and secretion, inflammation, and bone density in women with gestational diabetes mellitus (GDM).

WHAT THIS STUDY ADDS

We found that breastfeeding duration was associated with lower weight, weight retention, body fat, lean mass, visceral adipose tissue, insulin resistance, insulin secretion and C reactive protein, but not insulin resistance-adjusted insulin secretion, nor bone mineral density at 1 year postpartum in women after GDM.

Breastfeeding for more than 6 months improved cardiometabolic health in women after GDM.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Our study is the first to show that breastfeeding for more than 6 months is protective of adverse metabolic outcomes without compromising bone health in women with prior GDM. Future studies can expand on our findings to investigate the effect of breastfeeding on bone density in women with prior GDM. In practice, breastfeeding for more than 6 months, especially for women with GDM, should be encouraged by healthcare professionals.

Introduction

Women with gestational diabetes mellitus (GDM) have a more than sixfold increased risk of type 2 diabetes mellitus (T2DM) 1 and a twofold higher risk of cardiovascular diseases (CVDs). 2 Therefore, improving cardiometabolic health in this young, high-risk group is crucial.

Breastfeeding is a modifiable protective factor for cardiometabolic diseases and is associated with a lower future risk of glucose intolerance, T2DM, metabolic syndrome, and CVD, independent of known confounders. 3–5 Breastfeeding can also contribute to loss of weight and body fat. 6 In women with GDM, breastfeeding has been associated with lower postpartum body mass index (BMI) and improved insulin resistance. 7 However, there is lack of data on the impact of breast feeding on body fat, lean mass or bone mineral density (BMD) in this population. There are conflicting data regarding its effect on body composition and BMD in the general population. 8–13 While an intervention to promote breastfeeding duration did not result in lower percentage of body fat in a large cluster-randomized trial, 14 one cohort study showed a beneficial association between breastfeeding and fat mass index (FMI) in women having had a child in the last 5 years 9 and two prospective studies suggested that exclusive breast feeding was associated with lower fat mass than mixed breastfeeding. 15 16 In addition, breastfeeding has been associated with a lower visceral adipose tissue in some studies. 17–19 Although existing studies that investigated the relationship between breastfeeding duration and lean mass are limited, no significant relationships have been reported. 10 11

Although some studies have associated breastfeeding with a reduction in visceral adipose tissue (VAT), 17 18 this has not been shown in women with prior GDM. 20 21 Regarding insulin secretion (assessed by the disposition index), the impact of breastfeeding after GDM has shown a beneficial association in some, 21 22 but not all studies. 23 24 Similar inconsistency is observed regarding the association between breastfeeding and inflammatory parameters, 20 23 25 26 which tend to be higher in women with GDM compared with controls. 27 Studying the impact of breastfeeding on metabolic health parameters and inflammation in this population is important, since the underlying mechanism through which breastfeeding mitigates cardiometabolic risk remains unclear. Emerging research indicates that metabolic syndrome and inflammation are associated with decreased BMD. 28 29 Women with prior GDM have lower BMD 30 compared with healthy controls, as well as an increased risk of hip and other fractures later in life. 31 Additionally, breastfeeding is associated with a reduction in BMD, that may be transitory. 13 To date, there have been no studies on the impact of breastfeeding on BMD in women with prior GDM.

Existing studies in women with GDM are mostly retrospective, and rarely account for metabolic risk or protective factors prior to conception, lifestyle factors or adverse pregnancy outcomes that might influence breastfeeding initiation and duration. 32 Furthermore, there is no consensus on the recommended breastfeeding duration for improved maternal health, as the recommendation for 6-month exclusive breastfeeding by the WHO 33 and other international health organizations 34 is based on infant health. Therefore, and to investigate the effect of breastfeeding on overall health, we aimed to investigate the prospective associations between breastfeeding and maternal metabolic health, inflammation, and bone density at 1 year postpartum in a multiethnic cohort of women with prior GDM. We specifically evaluated weight, weight retention, body fat, fat-free mass, VAT, insulin resistance, and insulin secretion. We also tested if all these health outcomes differed between women who breastfed for ≥6 months vs <6 months.

Subjects, materials and methods

Study design and participants.

This prospective study is a secondary analysis of the ‘MySweetheart trial’ ( NCT02890693 ), which tested the effect of an interdisciplinary prepartum and postpartum lifestyle and psychosocial intervention in women with GDM. The study protocol has been previously described. 35 Of the 211 women included in the trial (n=105 in usual care, n=106 in the intervention group), the current analysis excluded women who had missing data on breastfeeding duration (n=40). All 171 women included completed the postpartum visit of 6–8 weeks and n=165 completed the 1-year postpartum follow-up.

GDM management and patient follow-up

Participants in the usual care group were followed according to the current guidelines of the American Diabetes Association and the Endocrine Society guidelines. 36 37 Women were first seen at 24–32 weeks of GA (gestational age) by a physician or diabetes specialist nurse, who then followed them until delivery. They received information on GDM, dietary advice by a registered dietician and tailored recommendations regarding lifestyle changes and gestational weight gain (GWG). 38 Glucose-lowering medication (insulin and/or very rarely metformin) was introduced if capillary glucose values remained above target (fasting glucose >5.3 mmol/L, 1-hour postprandial glucose >8 mmol/L or 2-hour postprandial glucose >7 mmol/L) despite lifestyle modifications. All women were counseled on the benefits of breastfeeding. At 6–8 weeks and 1 year postpartum, women underwent a 75 g oral glucose tolerance test (oGTT) and general advice on lifestyle counseling was provided, but no medication prescribed.

On top of the usual care, women in the intervention group had a total of nine individual interdisciplinary lifestyle sessions in the perinatal period, two workshops and a coach support mostly through telemedicine. It focused on tailored behavioral and psychosocial strategies to improve diet, physical activity, mental health, social support, and encouraged breastfeeding. 6

Breastfeeding duration served as the predictor, and we assessed metabolic, inflammatory, and bone density measures at 1 year postpartum.

Sociodemographic, descriptive health, and lifestyle characteristics

Sociodemographic variables including age, educational level, and ethnicity were assessed during the first GDM visit (24–32 GA). Data on medical characteristics including pre-pregnancy weight, parity, gravida, GDM in previous pregnancy, family history of diabetes, and medical treatment were extracted from the patients’ medical chart or assessed during a structured face-to-face interview.

Dietary intake was assessed with a validated Food Frequency Questionnaire (FFQ), developed for adults who live in the French part of Switzerland. Details of this FFQ are described elsewhere. 39 Physical activity (PA) was assessed with the GENEActiv accelerometer worn for 10 consecutive days on the right wrist. 40 Both the FFQ and the PA were assessed during pregnancy and at 1 year postpartum.

Pregnancy outcomes

GA at delivery, and adverse pregnancy outcomes including gestational hypertension, pre-eclampsia, placenta previa, cesarean section, prematurity, small and large for gestational age, 41 intrauterine growth restriction, and hospitalization in neonatology were extracted from the patients’ medical chart.

Breastfeeding duration

We assessed breastfeeding duration in months both at 6–8 weeks and at 1 year postpartum using a self-report questionnaire (without differentiating between exclusive and non-exclusive breastfeeding). If patients discontinued within the first month, we recorded a duration of 0.5 months, representing the median value between 0 and 1 month. When breastfeeding information was missing from the questionnaires (n=8), we relied on the information collected at the postpartum clinical visits at which women were asked if they were still breastfeeding, and if not, when they had stopped.

Metabolic health outcomes, inflammatory markers, and bone density

Anthropometry and body composition.

Pre‐pregnancy weight was extracted from participants’ medical charts or was rarely self‐reported at the first GDM visit if missing. We measured height and weight (Seca model 220, Hamburg, Germany) at the first GDM visit. Weight was also measured at the postpartum visits. Postpartum weight retention (PPWR) at 1 year was defined as the difference in pre-pregnancy weight and weight at 1 year postpartum.

Body fat and fat-free mass were estimated from reactance and resistance values according to Kyle’s equation, 42 using bioelectrical impedance analysis (BIA, Akern BIA 101) at 6–8 weeks and 1 year postpartum. At the 1-year postpartum visit, body composition (fat and lean mass) was assessed by dual X-ray absorptiometry (DXA) using Lunar iDXA (GE Healthcare) in 109 women who signed an additional consent form for this procedure. VAT was determined using DXA CoreScan software, and FMI (kg/m 2 ) was calculated as the ratio of fat mass (kg) to the square of height (m 2 ).

We measured HbA1c with a chemical photometric method (conjugation with boronate‐Afinion) at the first GDM visit, and with a high‐performance liquid chromatography method in the postpartum according to international guidelines. 43

Insulin resistance/secretion indices

Women underwent an oGTT at 1 year postpartum. We measured glucose and insulin values every 30 min over 2 hours to calculate insulin resistance and secretion indices. The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) 44 was used as a measure of insulin sensitivity and Homeostatic Model Assessment of β-cell index (HOMA-B) 45 as a measure of β-cell function. Whole body insulin sensitivity was estimated with the Matsuda index. 46 Absolute insulin secretion was estimated using area under the curve (AUCins/glu). 46 Insulin Secretion-Sensitivity Index-2 (ISSI-2), also known as disposition index, was expressed as the product of Matsuda index and AUCins/glu. 47 Women were advised to avoid moving, smoking and breastfeeding during the oGTT to avoid influencing the results. 48

Measurement of inflammation

We measured C reactive protein (CRP), tumor necrosis factor α (TNF-α), and interleukin 6 (IL-6) at 1 year postpartum. CRP was analyzed in serum aliquots using a latex-enhanced immunoturbidimetric assay on a Cobas 8000 autoanalyzer (Roche Diagnostics, Mannheim, Germany), with assay characteristics as reported by the manufacturer. TNF-α (U-PLEX Human TNF-α Antibody Set) and IL-6 (U-PLEX Human IL-6 Antibody Set) were measured using ELISA according to the manufacturer’s instructions.

Bone mineral density

BMD, and BMD T-score and Z-score of the vertebrae L1–L4 were assessed using Lunar iDXA. BMD was calculated in accordance with the International Society of Clinical Densitometry criteria. 49

Statistical analysis

All statistical analyses were performed with STATA/SE V.17.0 (StataCorp, Texas, USA). Sociodemographic and health characteristics were expressed as means (±SD) or number and percentages (%). Outcome variables were normally distributed, except for inflammatory markers (CRP, TNF-α and IL-6), for which natural log transformation was performed beforehand. Breastfeeding duration was considered a continuous variable (in months), or categorized into BF <6 vs BF ≥6 based on the median duration of breastfeeding in our sample (6 months) and in line with international recommendations to breastfeed for at least 6 months. 33 34 Potential sociodemographic, lifestyle and health-related confounders were chosen based on previous literature 32 50–52 and their potential relationship with our predictor (breastfeeding duration) and/or outcome variables (metabolic health, inflammation and bone density). We tested the following potential confounders: age, pre-pregnancy weight, pre-pregnancy BMI, weight at the first GDM visit, HbA1c at the first GDM visit, total caloric intake at first GDM visit, total PA at first GDM visit, time spent in moderate and vigorous PA at first GDM visit, ethnicity/nationality, educational level, parity, gravida, GDM in previous pregnancy, family history of diabetes, GWG throughout pregnancy, glucose-lowering treatment in pregnancy, pregnancy-induced hypertension, pre-eclampsia during pregnancy, placenta previa during pregnancy, cesarean section, prematurity, small for gestational age, intrauterine growth restriction, hospitalization in neonatology, food intake at 1 year postpartum, daily total PA at 1 year postpartum and time spent in moderate and vigorous physical activity at 1 year postpartum. Differences in potential confounders were assessed according to breastfeeding category (no or <6 months (BF <6) vs ≥6 months (BF ≥6) of breastfeeding), using an analysis of variance test for continuous variables or Χ 2 test for categorical variables. Potential confounders that were significantly different between our two breastfeeding groups (pre-pregnancy BMI, educational level and glucose-lowering treatment during pregnancy) were included as confounders in our further analysis. Predictors and outcomes (body composition, metabolic health, inflammatory markers and bone density) at 1 year postpartum did not differ between the intervention and usual care groups. Mean breastfeeding duration (predictor) was 7 months in both groups. Similarly, the results and effect sizes of the relationship between breastfeeding duration and the different outcomes were similar when we restricted the analyses to the usual care group. Therefore, we pooled participants from both groups to increase the sample size and adjusted for group allocation in all analyses.

We performed partial correlations and multiple linear regression analysis to determine the relationship between breastfeeding duration and outcome variables, adjusting only for group allocation (model 1). In a second model, we also adjusted for the aforementioned confounders that were significantly different between BF <6 and BF ≥6 groups (pre-pregnancy BMI, educational level, and need for glucose-lowering treatment during pregnancy).

We also performed a multiple linear regression analysis to determine differences in health outcomes according to category (BF <6 vs BF ≥6) using the same two regression models. We did not correct for multiple testing due to the high correlations between several of the health outcomes, in particular the metabolic ones, and the known potential impact of breastfeeding on various health outcomes. 53

In supplementary analyses, we investigated differences in adverse pregnancy outcomes as well as lifestyle variables at 1 year postpartum, according to breastfeeding category. For all statistical analyses, two-sided p<0.05 was considered to be statistically significant.

Mean age at the first GDM visit (n=171) was 33.5±5.8 years and mean pre-pregnancy weight was 68.6±14.7 kg ( table 1 ).

  • View inline

Sociodemographic and descriptive health characteristics of study participants according to breastfeeding category

Mean breastfeeding duration was 6.6 months, with a rate of breastfeeding initiation among the study participants of 94.2% (n=161). Specifically, 40.4% (n=69) of women either did not initiate (n=10) or breastfed for less than 6 months (BF <6), whereas 59.6% (n=102) breastfed for at least 6 months or longer (BF ≥6). The mean breastfeeding duration was 2.1 months in BF <6 women, and 9.7 months in BF ≥6 women.

BF <6 women had a significantly higher pre-pregnancy weight, pre-pregnancy BMI, and weight at first GDM visit compared with BF ≥6 women (all p≤0.01). In addition, they had a lower educational level and more frequently needed glucose-lowering treatment during pregnancy (all p≤0.02). There were no significant differences in adverse pregnancy outcomes such as pre-eclampsia, cesarean section, or neonatal hospitalization between BF <6 and BF ≥6 ( online supplemental table 1 ), nor in dietary intake or PA at 1 year postpartum ( online supplemental table 2 ).

Supplemental material

Associations between breastfeeding duration and metabolic health, inflammatory markers, and bone density.

Breastfeeding duration inversely correlated with body composition at 1 year postpartum ( table 2 ).

Relationship between breastfeeding duration and metabolic health, C reactive protein (CRP), and bone density at 1 year postpartum in women with prior GDM

Specifically, each supplementary month of breastfeeding was associated with 0.54 kg lower weight, 0.36 kg lower weight retention, 0.42–0.43 kg lower body fat (BIA or DXA), 0.26 kg lower fat-free mass (BIA), 0.22 kg lower lean mass (DXA), 0.11 kg/m 2 lower FMI, and 0.01 kg lower VAT at 1 year postpartum, independent of group allocation, pre-pregnancy BMI, educational level, or need for glucose-lowering treatment during pregnancy. Breastfeeding duration was also inversely correlated with changes in weight, body fat (BIA), and fat-free mass (BIA) between 6–8 weeks and 1 year postpartum (all p≤0.05). The correlations between breastfeeding duration and the aforementioned outcome measures varied in strength, ranging from r=−0.16 for changes in fat-free mass in the postpartum (BIA) to r=−0.34 for body fat (DXA; data not shown).

Regarding insulin resistance and secretion, breastfeeding duration was negatively associated with HOMA-IR, HOMA-B and AUC (p<0.01), but positively associated with Matsuda and the insulin resistance-adjusted insulin secretion (ISSI-2) (all p≤0.01) at 1 year postpartum in model 1. The relationship between breastfeeding duration with HOMA-IR and HOMA-B was independent of the above-mentioned confounders (model 2; p≤0.02).

Regarding inflammation, we observed a significant negative correlation between breastfeeding duration and CRP (p<0.01) at 1 year postpartum that was independent of tested confounders (p=0.04). Breastfeeding duration was not associated with either TNF-α or IL-6, neither with BMD, T-score, or Z-score of the L1–L4 vertebrae at 1 year postpartum.

Differences in metabolic health, inflammatory markers, and bone density according to breastfeeding category

Table 3 shows the differences and changes in metabolic health variables, inflammatory markers, and bone density at 1 year postpartum according to breastfeeding category.

Differences and changes in metabolic health, C reactive protein (CRP), and bone density at 1 year postpartum according to breastfeeding category in women with prior GDM

Compared with BF <6 women, BF ≥6 women had significantly lower weight, weight retention, body fat (BIA and DXA), and fat-free mass (BIA) (all p≤0.04) at 1 year postpartum independent of confounders (model 2). Notably, the weight of BF ≥6 women was 3.44 kg lower, weight retention was 2.40 kg lower, and body fat (BIA) was 2.72 kg lower compared with BF <6 women. In addition, BF ≥6 women had greater weight and body fat (BIA) loss between 6–8 weeks and 1 year (all p≤0.04). BF <6 women had increased insulin resistance (higher HOMA-IR and lower Matsuda) and higher insulin secretion (HOMA-B and AUCins/glu) compared with BF ≥6 women (all p≤0.04) at 1 year postpartum; however, these associations were not significant when we adjusted for confounders.

Compared with BF <6 women, BF ≥6 women had significantly lower CRP at 1 year postpartum, which was independent of confounders (p=0.046). There was no significant difference in TNF-α, IL-6, BMD, T-score, or Z-score between both breastfeeding groups.

In this multiethnic cohort of women with GDM, we prospectively investigated associations between breastfeeding and metabolic health, inflammation and bone density in the postpartum period. We observed that a longer duration of breastfeeding was associated with lower weight, weight retention, body fat, and VAT at 1 year postpartum. It was also associated with decreased fat-free mass, although to a lower extent. Despite this, it had no unfavorable impact on BMD. Breastfeeding duration was also related to lower CRP, and lower measures of insulin resistance (HOMA-IR) and insulin secretion (HOMA-B, AUCins/glu), as well as to increased overall insulin sensitivity (Matsuda) and insulin resistance-adjusted insulin secretion (ISSI-2). All the observed associations, except AUCins/glu, Matsuda, and ISSI-2, were independent of medical and sociodemographic confounder variables. Similar results were found when we compared breastfeeding for ≥6 months, as encouraged by international recommendations, to a shorter breastfeeding duration.

Associations between breastfeeding duration and lower weight, weight retention, body fat, and FMI during the postpartum are consistent with a previous meta-analysis showing that a longer breastfeeding duration of any intensity is associated with lower BMI among women with prior GDM, 7 but is in contrast to more recent studies. 20 25

In our study, breastfeeding duration was inversely associated with VAT. Even in the absence of PPWR, VAT has been shown to remain higher than before pregnancy 54 and is associated with an increased risk of cardiometabolic diseases. 55 Our results are in agreement with previous studies in women without GDM, 17 18 but not with two studies in women after GDM. 20 21 The small sample size (n=26), early evaluation after only 3 months, 21 as well as the differences in GDM criteria resulting in a population with an advanced glucose intolerance in these studies 20 21 might explain the differences.

Variations in study designs, dietary advice, and the cultural and ethnic backgrounds of participants can influence differences in weight loss after GDM. 20 25 The beliefs and practices regarding breastfeeding and concomitant or compensatory increases in food intake in this period may vary significantly. Our multiethnic participants received consistent dietary messages advising against substantial increases in food intake during breastfeeding. 35 These lifestyle adaptions together with longer breastfeeding might account for the pronounced effect of breastfeeding on weight and body composition observed in our cohort compared with other studies. For instance, women in our cohort displayed 0.36 kg lower weight per month of breastfeeding, compared with two studies that reported a weight retention reduction equivalent to 0.04–0.17 kg per month in the general population. 56 57

Our finding of an association between breastfeeding and decreased lean mass and fat-free mass is in contrast to a prospective study in women without GDM, which found no correlation, 11 but noted that breastfeeding women in their cohort had a high protein intake. Lean mass is associated with many health benefits, including increased metabolic rate, reduction in inflammatory markers, and increased BMD. 10 58 In our cohort, we found no relationship between breastfeeding and BMD at 1 year postpartum. Breastfeeding has been shown to reduce BMD, particularly within the first 6 months postpartum, but most studies show a full bone mass recovery afterwards. 59 Postulated reasons for this temporary loss are mainly calcium mobilization from the bones used for milk production, higher parathyroid hormone-related protein stimulating the release of calcium from the bones, lower calcium intestinal absorption and high prolactin levels suppressing the hypothalamic–pituitary–ovarian axis. 13 59 From our results, it appears that breastfeeding duration in women with GDM does not negatively impact BMD at 1 year postpartum (even in a context where 28% of women were still breastfeeding), aligning with results in women without GDM. These results are reassuring, especially since women with prior GDM have lower BMD 30 compared with healthy controls and have an increased risk of hip and other fractures later in life. 31 The long-term effects of breastfeeding on BMD in women with previous GDM warrant further investigation.

Breastfeeding duration was associated with lower insulin resistance (HOMA-IR) and lower insulin secretion (HOMA-B). Regarding HOMA-IR, our results are consistent with previous studies in women with GDM. 7 25 Other studies in this population have not shown an association between breastfeeding and insulin secretion such as HOMA-B, 20 25 although correlation was close to significance in one of them. 25 Unadjusted results in our cohort also showed an association between breastfeeding and the insulin resistance-adjusted secretion, the ISSI-2, consistent with two previous studies. These studies did not adjust for potential confounders, 21 22 and in our analysis, the association between breastfeeding and ISSI-2 was attenuated after adjusting for pre-pregnancy BMI. These results suggest that breastfeeding mainly improves insulin resistance but its impact on insulin secretion might be mediated through the effect on weight and potentially insulin resistance. Indeed, when we adjusted for HOMA-IR, the association between breastfeeding and ISSI-2, as well as HOMA-B, was not significant (data not shown).

In our cohort, breastfeeding duration, as well as breastfeeding for ≥6 months, was associated with decreased CRP, a marker of chronic low-grade inflammation. This association also persisted after adjustments for confounders, including pre-pregnancy BMI. These findings might further help to explain the relationship between breastfeeding and less cardiometabolic outcomes, as low-grade inflammation predicts increased adverse metabolic risk. 60 Previous research has shown mixed results regarding longer breastfeeding duration and lower postpartum CRP. 20 23 25 26 One study found a significant association between breastfeeding duration and lower CRP in middle-aged women with a history of GDM. 26 It is worthy to point out that in our cohort, breastfeeding was not associated with TNF-α or IL-6 at 1 year postpartum. Different mechanisms might be involved in the long-term and short-term regulation and expression of these inflammatory markers in the blood in women with GDM, suggesting that the impact of breastfeeding might not be the same.

Our study has several strengths. We prospectively followed women with GDM during pregnancy up to 1 year postpartum. To our knowledge, this is the first study to investigate the relationship between breastfeeding duration with diverse health outcomes including body fat, lean mass, BMD, and inflammatory markers at 1 year postpartum in women with prior GDM. Furthermore, we evaluated an exhaustive number of metabolic markers, to assess the overall effect of breastfeeding on metabolic health in women with prior GDM and included also bone health in the same population. Many potential confounding variables including adverse pregnancy outcomes and lifestyle parameters in the postpartum were investigated. Moreover, our study is the first to illustrate the beneficial effect of breastfeeding ≥6 months compared with <6 months in women with prior GDM. Potential limitations in our study include the lack of a control group (women without GDM). In addition, the lack of data on the frequency and exclusivity of breastfeeding is a limitation, considering the dose–response relationship between breastfeeding intensity and metabolic health in women. 16 61 62 Furthermore, 40 participants had missing data on breastfeeding duration (n=40) which could influence our results, as they had a higher pre-pregnancy BMI and HbA1c levels at the first GDM visit. As previous studies show the association between higher maternal weight and lower rates of breastfeeding initiation and shorter breastfeeding duration, 63 it is possible that patients with missing breastfeeding duration data would have had a lower mean breastfeeding duration. Finally, we did not account for potential confounders affecting BMD, such as vitamin D or calcium intake, which may limit some of the interpretability of our findings.

Conclusions

In this multiethnic prospective cohort of women with GDM followed during pregnancy up to 1 year postpartum, longer breastfeeding duration was associated with lower metabolic parameters including weight, weight retention, body fat, VAT, CRP, and lower insulin resistance at 1 year postpartum, independent of confounders. Each month of breastfeeding duration was associated with a 0.54 kg lower weight and 0.43 kg lower body fat. Our findings suggest that breastfeeding for ≥6 months is protective of these adverse metabolic outcomes. In addition, breastfeeding ≥6 months is desirable, not only for the infants, but also for the cardiometabolic health of these high-risk mothers with no observed adverse effect on BMD. Therefore, we suggest integrating breastfeeding for at least 6 months in clinical care recommendations for women after GDM. Healthcare professionals should emphasize the potential enhancement of the benefits of breastfeeding through concurrent healthy diet and lifestyle choices when advising women on breastfeeding.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and the study protocol was approved by the Human Research Ethics Committee of the Canton de Vaud (study number 2016–00745). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

Our sincere appreciation goes to our study participants and their children for their time and participation. We thank Deborah Degen, Dominique Stulz and Isabelle Cohen-Salmon who helped with data collection, as well as the clinical team and especially Magali Andrey, Olivier Le Dizès and Seyda Demircan, who participated in several intervention sessions and gave helpful input.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

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  • Data supplement 2

DYQ and JJP are joint senior authors.

Contributors IH, DYQ and JJP designed the study. IH analyzed the data, performed all the statistical analyses, and wrote the draft manuscript under the supervision of JJP and DYQ. EGR, LG, JG, AA and AH acquired the data. AA and EGR did the body composition analysis. AA, EGR, JG, SS, LG, KB and AH critically reviewed the manuscript. All authors saw and approved the final draft of this manuscript for publication. JJP had the idea of the cohort, supervised all the work, takes responsibility for the integrity of the data and the accuracy of the data analysis, and is the guarantor of this work and takes responsibility for the decision to submit this work.

Funding This study was funded by a project grant of the Swiss National Science Foundation (SNF32003B_176119) and by an unrestricted educational grant of Novo Nordisk, the Dreyfus Foundation and the Gottfried und Julia Bangerter-Rhyner Foundation.

Disclaimer The funders of this study had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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  • Review Article
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  • Published: 21 May 2024

Association of gastrointestinal microbiome and obesity with gestational diabetes mellitus-an updated globally based review of the high-quality literatures

  • Jiahui Li 1 ,
  • Min Wang 1 ,
  • Shuai Ma 1 ,
  • Zhong Jin 1 ,
  • Haonan Yin 1 &
  • Shuli Yang   ORCID: orcid.org/0009-0002-7297-5424 1  

Nutrition & Diabetes volume  14 , Article number:  31 ( 2024 ) Cite this article

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  • Gestational diabetes

The purpose of this review is to investigate the relationship between gastrointestinal microbiome, obesity, and gestational diabetes mellitus (GDM) in an objective manner.

We conducted a thorough and comprehensive search of the English language literatures published in PubMed, Web of Science, and the Cochrane Library from the establishment of the library until 12 December 2023. Our search strategy included both keywords and free words searches, and we strictly applied inclusion and exclusion criteria. Meta-analyses and systematic reviews were prepared.

Six high-quality literature sources were identified for meta-analysis. However, after detailed study and analysis, a certain degree of heterogeneity was found, and the credibility of the combined analysis results was limited. Therefore, descriptive analyses were conducted. The dysbiosis of intestinal microbiome, specifically the ratio of Firmicutes/Bacteroides, is a significant factor in the development of metabolic diseases such as obesity and gestational diabetes. Patients with intestinal dysbiosis and obesity are at a higher risk of developing GDM.

Conclusions

During pregnancy, gastrointestinal microbiome disorders and obesity may contribute to the development of GDM, with all three factors influencing each other. This finding could aid in the diagnosis and management of patients with GDM through further research on their gastrointestinal microbiome.

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Introduction.

Trillions of microbial cells essential to human health are present in the human body. The gastrointestinal microbiome, located on the surface of the intestinal mucosa, is the largest microbial ecosystem in the human body and is involved in epithelial homeostasis, energy harvesting and immune development [ 1 , 2 ], and has a major impact on nutrient and energy metabolism, with significant implications for human health [ 3 ]. Based on their relationship to host health, the gastrointestinal microbiome can be categorized into beneficial, harmful and conditionally pathogenic bacteria [ 4 ]. There is no single optimal gastrointestinal microbiome composition, as it varies from individual to individual, and it is important to maintain a healthy balance between the host and the pathogens in order to achieve optimal metabolic and immune functions [ 5 ]. Changes or ecological disturbances in the normal composition of the gastrointestinal microbiome are defined as an imbalance between commensal and pathogenic bacteria [ 6 ], and the gastrointestinal microbiome can regulate each other to maintain the abundance and diversity of the microbiome in a dynamic equilibrium [ 4 ]. Due to changes in the body’s endocrine status, immune function, age profile, dietary structure, environmental and genetic factors, the gastrointestinal microbiome can become dysbiotic and show changes in abundance and diversity, this can lead to the development of metabolic disorders such as obesity and diabetes [ 6 ].

Overweight and obesity are significant global health issues and are significant risk factors for GDM [ 7 ]. Overweight and obesity have a strong association with adverse pregnancy outcomes. An increase in pre-pregnancy body mass index (BMI) significantly increases the risk of fetal miscarriage, neonatal hypoglycemia, fetal malformations, large for gestational age, macrosomia, and cesarean section [ 8 , 9 ]. Obesity reduces the number of insulin receptors and causes receptor defects, leading to insulin resistance (IR) and higher fasting insulin levels. This affects glucose transport, utilization, and protein synthesis. Women are particularly susceptible to GDM due to excessive nutritional intake during pregnancy.

GDM is one of the most common complications of pregnancy and is defined as the first occurrence of diabetes during pregnancy due to abnormal glucose metabolism [ 7 , 10 ]. The International Diabetes Federation estimates that the global prevalence of diabetes will increase to 10.2 percent (578 million people) by 2030 and to 10.9 percent (700 million people) by 2045 [ 11 ]. In normal pregnancy, especially towards the third trimester of pregnancy(T3), beta(β) cells secrete more insulin to compensate and maintain normal blood glucose levels due to IR. However, in GDM, certain changes lead to a reduction in insulin sensitivity, impairment of insulin secretion and the development of carbohydrate intolerance [ 12 ]. The molecular mechanisms by which these changes take place are still not clear.

In recent years, research into the relationship among the gastrointestinal microbiome and obesity and GDM has become a major topic in medicine, but the results of these studies on the upregulation or downregulation of the gastrointestinal microbiome are not consistent because the subjects were from different countries and races, and the tests used were different. Due to the high degree of heterogeneity of the cohort studies in these areas, it was not possible to perform combined analyses. Therefore, this review aims to provide a comprehensive overview of these three areas of research, to further our understanding of GDM, to identify new targets for treatment of GDM, and ultimately to provide guidelines for clinicians.

Composition and characteristics of the gastrointestinal microbiome during pregnancy

The microbiome, which is the collection of all gastrointestinal microbial genes in an individual, is an order of magnitude larger than the human genome [ 13 , 14 ]. The adult intestinal is colonized by at least 1800 genera and approximately 15–36,000 species of bacteria [ 15 ]. The human gastrointestinal microbiome consists of four main groups: Firmicutes, Bacteroides, Actinomycetes, and Proteus [ 16 ]. The ratio of Firmicutes to Bacteroides(F/B) is a crucial parameter that reflects dysbiosis of the gastrointestinal microbiome [ 17 ]. According to molecular analyses targeting 16S rRNA, the fecal samples of healthy human volunteers contained bacteria primarily from two phyla: Firmicutes and Bacteroidetes. These bacteria were predominantly anaerobic species [ 18 ]. Compositionally, the gastrointestinal microbiome of the entire pregnant population is also dominated by members of Firmicutes and Bacteroidetes, which account for approximately 90% of the microbial environment in the intestine [ 5 , 19 ]. Currently, there is a great deal of variation in the study of gastrointestinal microbiome in various countries, possibly due to factors such as study populations from different races, different ages, the use of different test methods, contamination of study reagents, and statistical differences.

The role of bile acids

It is worth noting that bile acids (BA), which are a type of amphiphilic steroid produced in the liver and modified by the microbiome, are increasingly recognized as contributing to the pathogenesis and progression of metabolic diseases such as obesity and diabetes. BAs promote the absorption of intestinal fat. They also have hormone-like functions by activating nuclear and membrane-bound receptors, which regulate glucose, lipid and energy metabolism, intestinal integrity and immunity [ 20 ]. The BA biotransformation process is shown in Fig. 1 [ 21 ].

figure 1

Cholesterol is converted to primary BAs in the liver. Primary BAs are conjugated with primarily taurine in mice or glycine in humans before being transported to the gallbladder for storage in the form of bile. On ingestion of dietary fats, primary conjugated BAs (within bile) are released into the gut lumen to aid lipid absorption. Bacteria with BSH deconjugate BAs, thereby weakening their soap-like qualities. This allows other microbiome members to further modify them into secondary BAs. Some secondary BAs can be transported back to the liver, where they are then conjugated. The interaction between the gut microbiome and BAs leads to modulation of FXR and TGR5 agonists and antagonists, and thus, allows the gut microbiome to affect host metabolism (Note: T taurine, G glycine. In humans: TCA taurocholic acid, TCDCA taurochenodeoxycholic acid. In mice: TαMCA tauro-α-uricholic acid, FXR farnesoid X receptor, TGR5 G protein-coupled BA receptor 1).

Glucose homeostasis is a crucial physiological process affected by BAs. BA signaling pathways, such as the nuclear hormone receptor FXR [ 22 ] and TGR5 [ 23 ], are highly conserved metabolic regulatory pathways between mouse models and humans, and they affect IR. Additionally, BA regulates dozens of genes involved in metabolic homeostasis through the activation of fibroblast growth factor 19 in humans [ 24 ]. Studies in mice have shown that FXR regulates the release of glucagon-like peptide-1 (GLP-1) [ 25 ], This peptide promotes gluconeogenesis [ 26 ] and browning of white adipose tissue in the liver and muscle [ 27 ]. The influence of diet on BA signaling is also evident. Studies on FXR in various animal models of metabolic disorders have yielded inconsistent results. However, several studies have demonstrated that mice lacking FXR on a regular diet develop hyperglycemia and hypercholesterolemia [ 28 ]. Additionally, feeding FXR-deficient mice a high-fat diet prevented obesity and improved glucose homeostasis [ 29 , 30 ]. TGR5 is expressed in various organs including the gallbladder, lung, spleen, liver, bone marrow, and placenta [ 28 ]. It is also present in intestinal L cells, immune cells such as Kupffer cells, muscle, and brown adipose tissue [ 21 ]. TGR5 activation in brown adipose tissue promotes calorie production from stored fat. In L cells, TGR5 activation affects glucose homeostasis primarily through the secretion of GLP-1 [ 21 ].

Circadian rhythms also affect human glucose metabolism, with a 34% decrease in insulin sensitivity at night compared to the morning [ 31 ]. Disturbances in circadian rhythms are considered a significant factor in metabolic disorders. The roles of intestinal microbiome and BA metabolism have received significant attention. A recent study highlights the importance of maintaining proper oscillations of Ursodeoxycholic acid in a lean state to synchronize insulin sensitivity oscillations [ 32 ]. However, the function of the gut microbiota-bile acid axis in regulating circadian rhythms of metabolic homeostasis remains largely unknown and requires further exploration in the future.

The relationship among gastrointestinal microbiome, obesity and GDM

During pregnancy, around 20% of patients develop pre-diabetes or type 2 diabetes Mellitus [ 33 ]. Hormone levels undergo various changes during pregnancy, particularly with a significant increase in luteinizing hormone and estrogen levels, which produce many physiological effects. These hormone levels may affect the composition of the microbiome [ 34 ]. In addition, changes in modern lifestyles and the use of antimicrobial drugs have led to a decrease in the diversity of the gastrointestinal microbiome in many populations in developed countries [ 35 ]. Dysbiosis of the gastrointestinal microbiome can have several adverse effects on the organism, such as imbalances between Firmicutes and Bacteroidetes, which can lead to obesity and diabetes [ 36 ].

This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. We conducted a comprehensive search for both published and unpublished literature in English up to 12 December 2023, using databases such as PubMed, Web of Science, and the Cochrane Library. In order to gather the literature, we established rigorous inclusion and exclusion criteria and employed a search strategy that combined keywords with free words or synonyms.

Inclusion criteria

The study compared women during pregnancy with GDM (experimental group) to those with normal glucose tolerance (control group), while also examining differences between women with normal weight and those who were overweight or obese.

Fresh feces were collected from the subjects in the early morning, and fasting venous blood was drawn to analyze gastrointestinal microbiome and measure BMI.

The study included both experimental and control groups to investigate indicators of gastrointestinal microbiome, levels of obesity, and blood glucose levels.

Make sure that experimental and control groups were comparable.

Use randomized controlled trials, case-control studies, or cohort studies.

The literatures included in this study were written in English.

Exclusion criteria

Studies on women with multifetal pregnancies or less than 10 cases.

Studies for which the full text was not available or from which no valid data could be extracted.

This section excludes studies in the form of case studies, reviews, and lectures.

Considering that this is the first systematic review so far to study the association of gastrointestinal microbiome and obesity with GDM, this direction was registered with PROSPERO (registration number: CRD42023486272). After conducted a thorough literature search and analysis (see Table 1 for the search process), a total of 6 relevant documents were identified for this study (see Table 2 for the basic characteristics of the included documents), and there was less comparability and more heterogeneity between the groups, which made it impossible to perform a combined analysis. Considering the above, a descriptive analysis of these few high-quality documents is presented below.

It has been found that most studies on the involvement of gastrointestinal microbiome in metabolic diseases rely on the use of 16S rRNA marker genes [ 37 ]. The 16S rRNA genes of bacteria are made up of conserved and variable parts, the latter being specific to a given strain. Sequences from the V3 to V4 parts of the 16S rRNA gene are generally accepted as representative of all 16S rRNA sequences, and the analysis of sequences from the V3 to V4 parts of the 16S rRNA gene is used to determine the taxonomy of each bacterial species [ 38 ]. Only one of the above six papers [ 39 ] sequenced the V6–V8 variable region of 16S rRNA, but ultimately it was the 16S rRNA that was investigated as a means to speculate on its interaction with host metabolism.

Two studies have suggested a correlation between the composition of gastrointestinal microbiome and both GDM and weight status [ 36 , 40 ]. Based on different periods of pregnancy, Zhi-ying Song [ 4 ] concluded that significant differences in the species composition of the gastrointestinal microbiome existed between the purely overweight group and the group of normal women in T3. In a study of a group of people who were both obese or overweight, Marketa [ 41 ] concluded that significant differences in the gastrointestinal bacterial microbiota of different GDM pathogenesis groups existed in the first trimester of pregnancy. The two remaining studies [ 39 , 42 ] comparing the composition of gastrointestinal microbiome between GDM and women in pregnancy without GDM (non-GDM) groups found no significant differences. In their analysis of the intestinal microbiome and weight, Yao Su and colleagues [ 36 ] discovered that the ratio of F/B in the ultra-restructured gastrointestinal microbiome of GDM was approximately 3:5, which is the opposite of other groups. Zhi-ying Song [ 4 ] found a negative correlation between pre-pregnancy BMI and Lactobacillus, as well as between weight gain during pregnancy and Desulfovibrio in Proteobacteria. (Table 3 presents the specific differences in the indicators of gastrointestinal microbiome between the groups.)

The diagnosis of GDM is primarily based on a glucose tolerance test. This involves measuring fasting glucose levels, as well as glucose levels at 1 h (OGTT_1h) and 2 h (OGTT_2h) after consuming a glucose solution. (Criteria: At least one of the three blood glucose values must be equal to or greater than 5.1 mmol/L, 10.0 mmol/L, and 8.5 mmol/L, respectively.) The test is typically performed between 24 and 28 weeks of gestation and follows the guidelines developed by the International Association of Diabetes and Pregnancy Research Groups [ 43 ]. The correlation analyses conducted by Zhi-ying Song and others [ 4 ] found that the composition and abundance of gastrointestinal microbiome have an impact on GDM. Specifically, blood glucose values at OGTT_1 h and OGTT_2 h were positively correlated with Bacteroides, and negatively correlated with Prevotella. It is important to note that these findings are based on objective data and do not include any subjective evaluations. The study In Patricia [ 42 ] found a positive correlation between the abundance of Christensenellaceae and Enterobacteriaceae with plasma glucose levels one hour after OGTT. Conversely, Enterococci were negatively correlated with plasma glucose levels two hours after OGTT. These findings suggest potential implications for the diagnosis and treatment of GDM.

A cohort study by Dualib et al. [ 42 ] concluded that in relation to the development of GDM, alpha(α) and β diversity did not differ between the GDM and Non-GDM groups, despite differences in the relative abundance of specific bacteria. In our study, Thomas et al. [ 39 ] found that no significant changes in the relative abundance of major bacterial taxa were detected between women in health and women with GDM at 28 weeks’ gestation, and that the occurrence of GDM was associated with a decrease in Shannon diversity ( p  = 0.02) but no different clustering as measured by β-diversity, and that at 28 weeks’ gestation the women with GDM had a decreased microbial richness and evenness.

In Patricia’s study [ 42 ], the abundance of bifidobacteria and peptidococci increased in the third trimester of pregnancy, but there was no difference in α-diversity or overall microbiota structure between the two groups. These results suggested a degree of inconsistency, which could be due to differences in the subject populations, experimental error, or the comparability of the groups. Given that Patricia [ 42 ] included women who were overweight or obese regardless of when GDM was diagnosed by abnormal blood glucose, it is possible that the difference in BMI contributed to the increased gastrointestinal microbiome abundance, but this is only a preliminary hypothesis and further studies with larger sample sizes are needed to support this conclusion.

Research has indicated that individuals with a low abundance of gastrointestinal microbiome are more susceptible to dyslipidemia [ 44 ]. In this study, Thomas and colleagues [ 39 ] found that the GDM group had elevated levels of VLDL, triglycerides, venous glucose, HOMA-IR, and C-peptide at 28 weeks of gestation when compared to the control group. In a randomized controlled trial, Marketa [ 41 ] also found that Escherichia/Shigella had a positive correlation with plasma lipid levels, while Subdoligranulum had a negative correlation with plasma lipid levels in the GDM group. Coprococcus, Akkermansia, Methanobrevibacter, Phascolarctobacterium, and Alistipes were found to have a positive correlation with acetate, valerate, 2-hydroxybutyrate, and 2-methylbutyrate levels, respectively. In summary, these lipid metabolism abnormalities contribute to the development of obesity and GDM, but the specific indicators may vary among individuals.

Short-chain fatty acids (SCFAs) play a crucial role in glucose homeostasis by providing additional energy from undigested food. Butyrate, acetate, and propionate are the three main SCFAs produced by gastrointestinal microbes during the fermentation of nondigestible dietary fiber in the large intestine. SCFAs are most concentrated in the cecum and proximal colon, with concentrations decreasing towards the distal colon [ 21 ]. The type of SCFAs produced and the diet determine the metabolic pathways triggered through various receptors. SCFAs influence the regulation of host lipid and glucose metabolism through G protein-coupled receptors (GPCRs) linkages, such as GPR41 and GPR43 [ 45 ], as shown in Fig. 2 . Disturbances in gastrointestinal microbiome can lead to a reduced intestinal anti-inflammatory response. Low levels of SCFAs can also reduce the activation of GPCRs, leading to reduced activation of GPR41 and GPR43, which can generate intestinal inflammation, insulin resistance, and ultimately, diabetes [ 46 ]. Studies have shown that GPR43-deficient mice become obese even on a normal diet, while mice that specifically overexpress this receptor in adipose tissue remain lean [ 47 ]. Additionally, GPR43 activation promotes the secretion of GLP-1 in the intestine, enhancing insulin sensitivity [ 48 ]. SCFAs deficiency can cause a loss of tight junctions and increased enterocyte permeability. This can lead to increased absorption of bacterial endotoxins, such as lipopolysaccharide, which in turn can cause the production of pro-inflammatory cytokines. These factors can predispose women to IR and GDM [ 49 ].

figure 2

Lipid molecules regulate host metabolism through GPR41 and GPR43 receptor linkage.

Normal gastrointestinal microbiome has a positive impact on host metabolism. SCFAs activate GPCRs like GPR41 and GPR43. These receptors are expressed in various cell types, including intestinal epithelial cells, adipocytes, and immune cells [ 33 ]. Disruptions in the gastrointestinal microbiota can weaken the intestinal anti-inflammatory response. Low levels of SCFAs can also decrease GPCR activation, potentially resulting in intestinal inflammation, IR, and ultimately GDM.

Relationship between intestinal microbiome and GDM

Microbial abundance in women with GDM compared to non-GDM is either reduced [ 50 , 51 , 52 ], unchanged, or elevated [ 19 ]. However, there is currently no specific microbiota that can predict the development of GDM. Generally, pregnancy leads to increased bacterial loads and significant alterations in the composition of the intestinal microbiome [ 53 ]. The composition of intestinal microbiome during the first trimester of pregnancy(T1) is similar to that of women in health and nonpregnancy [ 53 ]. Microbiome disorders are highly characteristic in patients with GDM, particularly in mid-pregnancy(T2), and can be used as a predictor of GDM [ 54 ]. Significant alterations in the composition of the intestinal microbiome were observed in women during pregnancy compared to non-pregnant women, and from T1 to T3 [ 55 , 56 ]. The late pregnancy intestinal microbiome has been found to cause weight gain, insulin resistance, and a greater inflammatory response when transferred to germ-free mice compared to the early pregnancy microbiota [ 55 ]. In T3, Koren et al. found an increase in the abundance of the Actinobacteria and Aspergillus phyla, and a decrease in the Faecalibacterium [ 55 ]. Ferrocino et al. [ 54 ] discovered that individuals with GDM had an increased abundance of Blautia, Butyricicoccus, and Clostridium, as well as a decreased abundance of Bacteroides, Collinsella, and Rikenellaceae during T2 compared to T3 [ 19 ].

However, the gastrointestinal microbiome of patients with GDM may be abnormal at several levels, including the phylum and genus levels. According to a study [ 19 ], the GDM cohort had a higher abundance of Actinobacteria at the phylum level and Collinsella, Roseburia and Desulfovibrio at the genus level. According to Koren [ 55 ], significant changes are identified by a decrease in individual richness (α-diversity), an increase in intersubject diversity (β-diversity), and altered abundance of certain species.

An operational taxonomic unit (OTU) is a set of uniform markers used to represent taxonomic units, such as phylum, order, family, genus, and species. OTUs are created for the purpose of facilitating analysis in phylogenetic or population genetics studies [ 26 , 57 ]. In a comparative study of intestinal microbiome differences between healthy pregnant women and GDM patients, two studies analyzed 18 [ 58 ], and 17 [ 19 ] bacterial OUTs, respectively. The major differences in dysbiosis OUTs were attributed to the Firmicutes (72.2% and 88.2%). This suggests that alterations in the Firmicutes are a characteristic hallmark of GDM. Therefore, strains specific to the Firmicutes require urgent exploration in the future.

In a comparative study between women during pregnany in a normal state and those with diabetes, Xing et al. found that GDM subjects had a higher abundance of Lachnospiraceae family OTUs and a lower abundance of Enterobacteriaceae and Rumatococcaceae. Two Lachnospiraceae OTUs (247 and 672) were positively correlated with OGTT_1h at 24–28 weeks of gestation, and bacterial OTUs (e.g., Enterobacteriaceae_OTU 123 and Rumatococcaceae_OTU 93) were associated with FBG levels at 12 weeks of gestation [ 58 ]. Intestinal Lachnospiraceae bacteria have been suggested to be positively associated with type 2 diabetes Mellitus [ 59 ]. A study conducted in China found that, at the species level, the relative abundance of Clostridium_spiroforme, Eubacterium_dolichum, and Ruminococcus_gnavus was positively correlated with FBG, while Pyramidobacter_piscolens was negatively correlated with FBG [ 57 ]. A previous animal study also reported a positive correlation between Lachnospiraceae OTUs and blood glucose levels [ 60 ].

Ruminococcus gnavus is an anaerobic bacterium that is Gram-positive. It belongs to the Firmicutes, it is also a member of the family Lachnospiraceae. An increasing number of enteric and extra-enteric diseases are associated with this bacterium [ 61 ]. Zhi-ying Song [ 4 ] and Yao Su [ 36 ] found that g-Ruminococcus was a characteristic biomarker for the normal pregnant women group and Ruminococcaceae_UCG014 was a characteristic biomarker for the GDM-only group, respectively. The study suggests that Ruminococcaceae play a role in energy metabolism, insulin signaling, and inflammatory processes. It also found that an increase in the relative abundance of Ruminococcaceae is associated with higher FBG concentrations and TR, which increases the risk of developing GDM [ 62 ]. These findings indicate the potential predictive value of specific microbial combinations for GDM.

There is no predictive value of blood glucose values for the development of severe GDM. Furthermore, differences in the severity of GDM correspond to changes in gastrointestinal microbiome [ 63 ]. In a prospective longitudinal study conducted in Chiang Mai, Thailand, there were no differences in FBG, OGTT_2h, and glycated hemoglobin levels at diagnosis between patients with diet-controlled GDM and those requiring insulin therapy [ 63 ]. However, unlike the literature included in this study, GDM patients who ultimately required insulin therapy had higher levels of Clostridium difficile [ 63 ]. Lactobacillus has long been considered beneficial to the host by attenuating intestinal mucosal barrier dysfunction, remodeling intestinal microbiota composition, and reducing systemic inflammation [ 64 , 65 , 66 ]. However, there are conflicting accounts of its effects on GDM patients.

This study concludes that g_Lactobacillus may be a characteristic gastrointestinal microbiome of overweight GDM patients, distinguishing them from other patient groups [ 4 ]. A cross-sectional study found that GDM patients had lower levels of Lactobacillus casei than non-GDM controls before delivery [ 67 ]. Another study showed that the relative abundance of specific Lactobacillus at diagnosis was higher in women with GDM than non-GDM [ 68 ]. The varying results regarding different bacilli and blood glucose levels can be attributed to different subgroups of bacilli sequences. Therefore, it is important to explore the microbiome further in order to gain a better understanding of the role of various bacilli in female patients with GDM.

The relationship between gastrointestinal microbiome and obesity

Animal and human studies have shown that obesity is associated with an imbalance or ecological dysbiosis of the intestinal microbiome [ 69 ], as the imbalance between energy consumption and depletion favors the prevalence of disease-causing bacteria [ 70 ], but the role of the intestinal microbiome in the development of this disease and whether there is a causal relationship remains controversial [ 37 ]. The gastrointestinal microbiome plays a crucial role in the absorption of nutrients, in digestion and metabolic activities, as well as in the efficiency and storage of energy [ 71 ]. However, changes in the composition of various factors caused by the microbiota (ecological dysbiosis) may have adverse long-term effects, leading to diseases such as obesity, intestinal inflammation, diabetes and metabolic syndrome in the host organism and in future generations [ 72 ]. The effect of the intestinal microbiota on host metabolism was first demonstrated in a 2004 study of germ-free mice, which found that conventionally raised mice had more total body fat than those raised in germ-free conditions [ 73 ]. Studies evaluating the increased ratio of F/B following microbiota transplantation in obese individuals have not yet produced consistent results [ 74 , 75 ].

The maternal intestinal microbiota composition changes during pregnancy and breastfeeding due to maternal metabolism adjustments caused by the increased demands of the developing fetus and postnatal infant, as well as the mother’s own physiological changes [ 76 ]. Dysbiosis of the intestinal microbiome is prevalent in obesity and is characterized by a reduction in the diversity [ 44 ] and abundance of the intestinal microbiome in obese individuals [ 77 ]. Studies have shown that individuals with low intestinal microbiome abundance are more susceptible to obesity, IR [ 78 ], and dyslipidaemia [ 44 ]. In patients with obesity, the levels of mucinophilic Akkermansia muciniphila, Faecalibacterium prausnitzii, and Bacteroides were found to be decreased [ 79 , 80 , 81 ], while the abundance of fungal phyla was significantly increased [ 82 , 83 , 84 ].

A follow-up study was conducted in Finland with 256 women. The study found that overweight and obese mothers had a higher relative abundance of the Firmicutes, and there was a trend towards a higher ratio of F/B [ 85 ]. Additionally, the ratio of F/B decreased after weight loss in obese individuals [ 83 ]. It has been suggested that the higher relative abundance of Prevotella detected in women during pregnancy with obesity, compared to those who are overweight, may contribute to glucose metabolism through the metabolites produced [ 86 ].

Akkermansia is a Gram-negative, anaerobic, elliptical bacterium that degrades mucin and inhabits the outer mucus layer of the intestinal barrier [ 37 , 87 ]. The mechanisms by which mucin regulates obesity and glucose levels have not been fully elucidated. In humans, its abundance and genetic richness are positively correlated with healthy metabolic states, including better body fat distribution and absence of metabolic syndrome [ 88 , 89 ]. A previous study demonstrated that Akkermansia enhances thermogenesis and GLP-1 secretion, while reducing the expression of proteins involved in adipocyte differentiation. Additionally, it decreases the gene expression of glucose and fructose transporter proteins in the jejunum, indicating a reduction in carbohydrate absorption [ 90 , 91 , 92 ]. In conclusion, while most studies suggest a beneficial role for Akkermansia in metabolic profiling, its effects may be dual depending on dietary patterns [ 37 ]. Moran noted that consistent observations in the human intestinal microbiota and its interactions with diet and genetics suggest that the microbial diversity of individuals with a high BMI or obese individuals is lower. According to the study, Christensenellaceae, Oscillospira, and Rikenellaceae were more prevalent in individuals with a normal body weight, while Bifidobacteria and Akkermansia were less abundant in those with altered metabolism [ 37 ].

Relationship between obesity and GDM

Maternal adiposity increases significantly during pregnancy due to the increased nutritional needs of the fetus and the demands of the mother’s own metabolism. This can lead to GDM due to the development of IR. Obesity can also affect GDM through other mechanisms, including impaired β-cell function and chronic low-grade inflammation [ 93 , 94 ]. This inflammation is mainly manifested as dyslipidemia and a pro-inflammatory state during pregnancy [ 95 , 96 ]. Prospective studies have linked a range of fatty acids, phospholipids, lipoproteins, certain glycolipids, and cholesterol with incident GDM [ 97 ]. In a recent study of 1008 women’s lipidomic, it was observed that seven out of ten lipids associated with BMI (four LPCs, two TGs, and one SM) were linked to the risk of GDM, even after adjusting for maternal BMI [ 98 ]. Additionally, gestational weight gain has been experimentally confirmed to be associated with an increased risk of developing GDM [ 99 ]. A Danish study found that 11 OTUs were associated with gestational weight gain, with the majority being Clostridiales (7 of 11 OTUs), when diabetic status was not taken into account. Lower weight gain was associated with 7 OTUs, including a Christensenella OTU (OTU_63) and an Alistipes OTU (OTU_128). Weight gain was associated with 4 OTUs, including an Eisenbergiella OTU (OTU_258) and a Lactobacillus OTU (OTU_80) [ 19 ].

Stored upper body fat in pregnant women with obesity can increase the concentration of free fatty acids and lipotoxicity. This can lead to inflammation, endothelial dysfunction, and reduced trophoblastic invasion, ultimately decreasing placental metabolism and function [ 100 ]. The placenta serves as the sole interface between the mother and the fetus, making it a crucial organ for the exchange of gases and nutrients between the two. Specific changes occur in the structure of the placenta in pregnant women who are obese and diabetic, including increased weight, angiogenesis, and slower chorionic villus maturation. These structural abnormalities lead to functional abnormalities, which worsen metabolic abnormalities during pregnancy [ 101 ]. Abnormal protein expression in the placenta can cause insulin antagonism, resulting in abnormal insulin resistance and glucose metabolism [ 102 ]. Five proteins, namely very low density lipoprotein receptor, aquaporin-1, platelet factor 4, peptidyl prolyl isomerase, and malonyl cofactor-acyl carrier protein transacylase, have been associated with altered placental function, placental vascular dysfunction, and placental inflammation and its complications in patients with GDM [ 103 , 104 , 105 ]. Reduced very low density lipoprotein receptor levels may promote GDM by inhibiting the placenta’s ability to remove cholesterol [ 103 ]. Maternal IR is a pathophysiological condition that causes changes in the growth and efficiency of the placenta in pregnant women, especially those who are obese and diabetic [ 106 ]. IR may enhance chorionic cell proliferation and increase placental size, but expansion of immature chorionic villi may reduce the efficiency of placental transport mechanisms, leading to placental insufficiency [ 107 , 108 ]. Furthermore, during pregnancy, the placenta secretes pregnancy-specific hormones such as human chorionic gonadotropin, human placental lactogen, and human placental growth hormone, as well as increased levels of prolactin, estradiol, and cortisol into the maternal circulation. These hormones can affect glucose metabolism and lead to the development of diabetes. It is important to note that this is a complex process and further research is needed to fully understand the mechanisms involved. The rapid recovery of glucose homeostasis immediately after placenta expulsion at delivery demonstrates the significant role of the placenta in GDM with obese patients.

Recent data suggest that exosomes, which are membrane-derived nanovesicles, may play a role throughout pregnancy. This includes mediating placental responses to hyperglycemia and insulin sensitivity. Patients with GDM have been found to have higher levels of circulating exosomes, both overall and of placental origin, during gestation compared to normal pregnancies [ 109 ]. Additionally, hyperglycemia has been shown to increase the release of exosomes from trophoblast cells in early pregnancy [ 110 ], indicating a correlation between maternal metabolic status during pregnancy and circulating levels of placental exosomes.

It has also been suggested that hyperactivation of adipose tissue plays an important role in the pathogenesis of GDM. Lipocalin is a protein produced in large quantities by adipose tissue. It enhances insulin sensitivity, exerts anti-inflammatory effects, and reduces plasma glucose levels [ 111 ]. Lipocalin is thought to play a pivotal role in the regulation of systemic glucose homeostasis [ 112 ]. Lipocalins are expressed and synthesized primarily in maternal adipose tissue, but not via the placenta, and do not enter the fetal circulation [ 113 ]. Deletion of the lipocalin gene leads to impaired insulin tolerance [ 114 ]. Previous studies have shown that low levels of lipocalin in pregnant women are associated with reduced maternal insulin sensitivity during pregnancy [ 115 ]. These findings suggest that lipocalin may play a role in insulin sensitivity during pregnancy. In studies conducted in various populations, including South India [ 116 ] and Iran [ 117 ] maternal serum lipocalin levels were significantly lower in patients with GDM. The use of lipocalin as a prognostic biomarker for GDM risk is currently a topic of debate due to inconsistent results.

This review presents an overview of the gastrointestinal microbiota and its connection to obesity and diabetes (refer to Fig. 3 ), with a causal relationship between these three metabolic conditions. Differences between countries, regions, and ethnicities were analyzed. Currently, no specific combination of gastrointestinal microbiota has an absolute advantage to the host. However, there is some predictive significance for the degree of obesity and the severity of GDM based on the up- or down-regulation, expression or non-expression, and changes in abundance and diversity of different gastrointestinal microbiomes. The placenta plays a pivotal role during this particular period of pregnancy. Additionally, research on BA and intestinal flora mechanisms is a current topic in the field of metabolic diseases. The detection of gastrointestinal microbiome is becoming increasingly important. It can be used as a clinical indicator to assist in the diagnosis of obstetrics and gynecology, especially in patients with GDM.

figure 3

Association of intestinal microbiota and obesity with GDM.

However, this study also has some limitations: The investigation did not cover whether the offspring of mothers with GDM have abnormal intestinal microbiome; Additionally, this review did not discuss the potential of probiotics to treat intestinal dysbiosis due to length constraints; Furthermore, the limited number of current literatures on the relationship between the three factors makes it impossible to carry out a meta-analysis. Considering the aforementioned limitations, it is expected that future studies will concentrate on this aspect to provide advantages to patients with GDM.

Throughout the extensive history of studying gastrointestinal microbiome, it has been discovered that certain combinations or individual microbes have significant effects on host metabolism. This study analyzed high-quality and related literatures to identify that an imbalance in the F/B ratio may be a characteristic feature of intestinal microbiome dysbiosis, as outlined below: 1. An imbalance of the F/B ratio may lead to metabolic disorders such as obesity and diabetes; 2. The F/B ratio has been found to decrease with age, which may result in decreased glucose tolerance; 3. It can be concluded that the alteration of the Firmicutes is a characteristic marker of both GDM and obesity. Additionally, a characteristic biomarker of GDM, Ruminococcus gnavus, was found. However, the up- or down-regulation of the Firmicutes did not consistently affect the development of the disease. Studies have shown conflicting results regarding the F/B ratio in obese and GDM patients, with some suggesting a decrease and others suggesting a trend towards an increase in overweight or obesity. Despite some results from animal studies contradicting the transplantation of microbiota in obese populations, further research has shown promise. With the deepening and refinement of animal studies, as well as large-scale population-based studies (which are mainly retrospective at present), it is believed that specific changes in human intestinal microbiome (especially in pregnant populations) will be explored. This will be a milestone for early identification of clinical diseases and effective monitoring of health status.

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The Fig. 1 was reproduced with permission from [Fogelson KA, Dorrestein PC, Zarrinpar A, Knight R], [The Gut Microbial Bile Acid Modulation and Its Relevance to Digestive Health and Diseases]; published by [Gastroenterology], [2023]. And no changes were made. Please refer to https://creativecommons.org/licenses/by/4.0/ . The Figs. 2 and 3 used in this review were created with BioRender.com.

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Li, J., Wang, M., Ma, S. et al. Association of gastrointestinal microbiome and obesity with gestational diabetes mellitus-an updated globally based review of the high-quality literatures. Nutr. Diabetes 14 , 31 (2024). https://doi.org/10.1038/s41387-024-00291-5

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