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

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

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

Design Systematic review and meta-analysis.

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

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

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

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

Review registration PROSPERO CRD42021265837.

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Introduction

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

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

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

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

Search strategy and selection criteria

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

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

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

Data extraction

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

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

Risk-of-bias assessment

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

Data synthesis and analysis

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

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

Patient and public involvement

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

Characteristics of included studies

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

Search and selection of studies for inclusion

Characteristics of study population

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Associations between gestational diabetes mellitus and adverse outcomes of pregnancy

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

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

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

Subgroup, meta-regression, and sensitivity analyses

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

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

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

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

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

Principal findings

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

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

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

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

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

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

Strengths and limitations of the study

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

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

Conclusions

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

What is already known on this topic

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

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

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

What this study adds

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

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

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

Ethics statements

Ethical approval.

Not required.

Data availability statement

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

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

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

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

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

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

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

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

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Gestational Diabetes Mellitus: Review Essay

• To find inspiration for your paper and overcome writer’s block
• As a source of information (ensure proper referencing)
• As a template for you assignment

Gestational diabetes mellitus (GDM) is a very serious condition that affects the health of the mother, as well as her baby in varying time periods: immediate, short-term, or long-term. It is a type of diabetes that affects pregnant mothers and has the potential to recur despite the fact that it mainly disappears after birth. This paper is aimed at enhancing the understanding of GDM among Australian pregnant mothers with a focus on its prevalence, causes, health implications, established policies and public initiates aimed at addressing it, as well as future strategies and approaches that could help reduce its incidence and prevalence.

The prevalence of gestational diabetes worldwide has shown a general increasing trend in the last 20 years across race/ethnicity groups. According to recent data by Ferrara (2007), an increase of approximately 10 to 100% has been reported in various race/ethnicity groups. In Australia, the prevalence of GDM is estimated to range between 5.2 and 8.8% (Cheung & Byth 2003). The 2005-6 gestational diabetes mellitus in Australia report gave a figure of 4.6% to represent the fraction of pregnant women aged 15-49 years with GDM. This was a 20% increase compared with what had been recorded in 2000-1 (Templeton & Pieris-Cladwell 2008). According to this report, the incidence of gestational diabetes was increasing. This is seconded by Ferrara’s (2007) in his research work, in which he showed increasing trends in the prevalence of GDM in various geographical regions where South Australia was part. On an annual basis, 16,500 women are diagnosed with GDM (Dodd et al. 2007). Unfortunately, this is expected to continue growing.

There is no definite known cause for GDM but there are different facts and theories presented to guide this. One version is that during pregnancy, the hormones responsible for foetal growth and development impede the action of insulin hence insulin resistance (Australian Government and Diabetes Australia 2010). Based on this theory, it is automatically presumed that when the release of the blocking hormones stops, then the insulin levels go back to normal. As a result, it lacks a definite cause. Alternatively, risk factors are used to explain the cause of GDM as stated by Jovanovic & Pettitt (2001). These risk factors among others include obesity, maternal age, and a family history of diabetes. The fact that a pregnant woman gets GDM for the first time during their pregnancy is a risk factor in itself because histories of GDM are associated with high chances of developing the disease in subsequent pregnancies (Hall 2001).

Racial differences are also very imperative in determining the occurrence of GDM. This can be supported by the fact that the prevalence of GDM was reported to be higher in Chinese and Indian women residing in Australia compared with the women of European or Northern African descent, who were residing in Australia as well (State Government of Victoria 2012). In addition, the Aboriginal women rather than the non-Aboriginal women were more exposed to this type of diabetes (Ishak & Petocz 2003). The 2005-6 GDM report simply states that the incidence of GDM among women who had been born from other countries was twice as large as the incidence of those women born in Australia. Those born in Southern Asia were 3.4 times more at risk of getting the disease compared with those born in Australia (Templeton & Pieris-Caldwell 2008).

Gestational diabetes mellitus is a public health issue with serious implications. As stated earlier, its implications are felt almost immediately, in the short-term, or long-term. The implications of GDM are mainly felt by the infants in the newborn period. This is because of the current patterns that show an increase in the prevalence of diabetes in offspring born to mothers with GDM (Ferrara, 2007). Short-term-effects mainly include those that are observable or detectable during pregnancy, labour and a short time after birth and inexhaustibly include outcome of pregnancy, intensive care admissions, duration of pregnancy, need for resuscitation, method of delivery, foetal growth characteristics, and type of labour (Australian Institute of Health and Welfare 2010).

Neonates born to mothers with GDM experience the implications of GDM in the following ways: increased exposure to stillbirth, respiratory distress syndrome, caesarean section, shoulder dystocia, and macrosomia (Gonzalez-Quintero et al. 2007). Stone et al. (2002) implies that babies born to mothers with GDM are more at risk of going through the effects of this disease compared with babies of non-GDM mothers. This is due to the facts presented; whereas 17% of neonates from mothers with GDM were macrosomic, only 10% of the neonates from mothers without GDM had the condition, 13% of the newborns from mothers with GDM had neonatal jaundice compared with 7% of non-GDM mothers, and 32% of the newborns from mothers with GDM were delivered by caesarean compared with 19% of newborns belonging to mothers without GDM. Suhenon & Teramo (1993) indicate that GDM exposes pregnant mothers to pregnancy-induced hypertension and pre-eclampsia, operation during delivery, and induced labour. According to a study carried out in Victoria in 1996, 37% of women with GDM compared with 23% of women without GDM had induced labour. In addition, 41% of the women with GDM underwent operative delivery by means of vacuum extraction, forceps, or caesarean as opposed to 29% of women without GDM (Stone et al. 2002).

The long term implications of GDM to the mothers include increased risk of recurrent GDM in subsequent pregnancies. In addition, it results in progression to type 2 in these mothers and a general resultant effect of high prevalence of type 2 diabetes in general. It is has been estimated that 17% of Australian women with GDM are later diagnosed with type 2 diabetes within 10 years. These figures can go up as high as 50% when the timeframe changes to 30 years (Lee, et al., 2007; Metzger 2007). The explanation behind this is that the prevalence of GDM has been indicated as a reflection of the prevalence of type 2 diabetes in the larger population. In addition, it is the attributive risk factor for type 2 diabetes among the pregnant mothers with GDM (Kim, Newton & Knopp, 2002).

GDM posses as a serious health risk for pregnant mothers because it also exposes them to heart diseases according to Retnakaran & Baiju (2009). Women with GDM have an increased risk of neonatal hypoglycaemia, and hyperbilirubinaemia. The babies are also affected in the long term because they tend to have congenital anomalies. They also have an increased risk of obesity, impaired glucose tolerance, and are also susceptible to type II diabetes in early adulthood (Fetita et al. 2007).

As a result of the need to reduce associated co-morbidities and death, there have been initiatives put in place to ensure that GDM is reduced and lives are saved. The government has been involved in funding Diabetes Australia for the successful development and execution of effective mechanisms to reduce the incidence and prevalence of GDM (Australian government 2012). The National Diabetes Services Scheme (NDSS) is a project that was initiated by the Australian government through Diabetes Australia, and its role has been greatly recognized in as far as reduction and prevention efforts of GDM are concerned. Within the National Diabetes Services Scheme, there is the National Gestational Diabetes Register that was set up to enable women with GDM to gain control over their conditions and ensure that their health conditions do not worsen (Diabetes Australia, 2012).

Pregnant women are required to register with this body, the National Gestational Diabetes Register, and in return they are to receive some benefits. Their doctors and they are sent consistent reminders of the need to engage in diabetes checks. In addition, this body is involved in sensitizing and providing information in printed form to the women on the need of, and how they should adopt a healthy lifestyle. However, this does not guarantee that the women will actually read and understand the information. Therefore, it could be a reason for the continued increase because even though there is sensitization, the manner in which it is carried out matters a lot.

As indicated earlier, one’s lifestyle is a great determinant to one’s health and especially diabetes including GDM. Sometimes, individuals are not knowledgeable in as far as healthy diets are concerned, or they may not realize the essence of such diets. Consistent provision and distribution of printed materials on GDM and how it can be controlled is assumed to act as consistent reminders on the need to ensure that one adopts and practices healthy feeding habits. But this medium of passing information is questionable. This registration has been made free and therefore every pregnant woman is not restricted by money to get the reading materials, and engage in medical check-ups. Regardless of this free service, thoroughness in terms of follow-ups should be observed because the women act out of their own will and it might not be consistent. Such free services deserve more emphasis and should not be viewed as opportunities for only the less privileged because they are equally important to everyone.

Registration with NDSS also enhances one’s access to various products such as testing strips, insulin syringes and pen needles, and insulin pump consumables. These devices are very important in the management of GDM through monitoring of one’s glucose levels. It should not just be a matter of distributing these devices because if someone does not know how to effectively and properly use and take of this equipment, it would be a goalless venture. Ensuring that there is available data on the incidence and prevalence rates of pregnant women with GDM is another initiative. This data has been made available in the Gestational diabetes mellitus in Australia report. The first one was developed in 2005-6 and it aimed at providing researchers, scholars or related academicians with information on the incidence of GDM among women giving birth in hospitals. This report also provided information on high-risk sub-groups, which are defined on the basis of their orientation towards the condition. Factors mentioned in this report that determine inclination towards the disease include age and genes (Templeton & Pieris-Caldwell 2008).

A realization is that despite the strategies in place currently, the prevalence of GDM is still increasing. This could mean several things but in future, there is need to carry out researches and establish the effect of each strategy in reducing GDM and therefore establish their effectiveness, as well as what is not addressed by the various strategies. The world is constantly changing and therefore, there is need to constantly review and update data. Researchers should exercise validity because there lacks consistency in the levels of incidence and prevalence of GDM. Yet, this is very important in planning and allocating resources to address GDM in the future. Accurate figures enable the government and various bodies addressing diabetes to focus. I second Lancaster (1996) on the essence of enhancing the research area so as to give consistent and reliable results.

The main challenge is usually maintenance. Once the glucose levels have gone down, there is a tendency for women to assume that they are okay and therefore tend to ignore the monitoring bit. This is a reflection of the NDSS scheme where follow-up lacks and therefore, this element should be given full attention. There is need to clearly point out the essence and ways of maintaining a normal glucose level and especially for those mothers who have experienced GDM before so as to avoid its recurrence. Lack of follow-up is an indication of lack of seriousness. The notion by health care workers that diabetes is just like any other disease, and that they do not put much seriousness are elements that may have certain effects on the patients. As a result, the women may miss out on some fundamental practices that can affect their prognosis and road to recovery.

A lot of emphasis is mainly placed on the pharmacological dimension of GDM yet behavioural interventions such as healthy eating, engagement in healthy physical activity and proper stress coping mechanisms are more effective in reducing the recurrence of the disease but are not accorded the required attention and emphasis (Australian Government and Diabetes Australia 2010). There should therefore be a shift in emphasis where women should be really encouraged to change their lifestyles and adopt healthier ones, or maintain the healthy ones. Counselling services should be offered affordably, or as part of the NDSS package to ensure that all mothers benefit. To enable such utilization of services to enhance behaviour change, there is need to evaluate the accessibility of health care services among the GDM mothers and thereby figure out if there is a way that accessibility could be enhanced. Examples here would include subsidizing health care costs with a focus on the individual’s background financial position.

Future strategies should not focus solely on the GDM but instead, they should also encompass the associated type of diabetes that results. In essence, the strategies to address GDM should be designed in such a manner that also minimizes the occurrence of type II diabetes. The successful implementation of projects and policies is grounded in integration and co-operation among the involved parties. The various health care systems entail various departments and all these should liaise effectively with one another through effective communication to avoid inefficiencies and deficiencies in the system. These inefficiencies and deficiencies are so serious such that regardless of the great advocacy and accessibility to health care, lack of co-ordination results in poor delivery of services and may not produce the desired effect on the patient.

Australian Institute of Health and Welfare 2010, Diabetes in pregnancy: its impact on Australian women and their babies , Diabetes series no. 14. Cat. no. CVD 52, AIHW, Canberra.

Australian Government and Diabetes Australia 2010, Gestational Diabetes: Caring for yourself and your baby , Web.

Cheung, NW & Byth, K 2003, “The population health significance of gestational diabetes”, Diabetes Care, vol. 26 , pp. 2005-9.

Diabetes Australia 2012, Gestational Diabetes , Web.

Dodd, JM, Crowther, CA, Antoniou, G, Baghurst, P & Robinson, JS 2007, “Screening for gestational diabetes: the effect of varying blood glucose definitions in the prediction of adverse maternal and infant health outcomes”, Aust N Z J Obstet Gynaecol , vol. 47, no. 4, pp. 307-312.

Ferrara, A 2007, “Increasing Prevalence of Gestational Diabetes Mellitus: A Public Health Perspective”, Diabetes Care , vol. 30, no. 2, pp. S141-S146.

Fetita, L, Sobngwi, S, Serradas, P, Calvo, F & Gautier, J 2007, “Review: Consequences of fetal exposure to maternal diabetes in offspring”, Journal of Clinical Endocrinology and Metabolism, vol. 91, no. 10, pp. 3718–3724.

Gonzalez-Quintero, VH, Istwan, NB, Rhea, DJ, Rodriguez, LI, Cotter, A, Carter, J, Mueller, A & Stanziano, GJ 2007, “The impact of glycemic control on neonatal outcome in singleton pregnancies complicated by gestational diabetes”, Diabetes Care, vol. 30, no. 3, pp. 467–470.

Hall, LD, Sberna, J & Utermohle, C 2001, “Diabetes in pregnancy, Alaska 1990–1999”, State of Alaska Epidemiology Bulletin, vol. 5, no. 3, pp. 1–9.

Ishak, M & Petocz, P 2003, “Gestational diabetes among Aboriginal Australians: prevalence, time trend, and comparisons with non-Aboriginal Australians”, Ethnicity and Disease, vol. 13, pp. 55–60.

Jovanovic, L, & Pettitt, DJ 2001, “Gestational diabetes mellitus”, JAMA, vol. 286, pp. 2516–2518.

Kim, C, Newton, KM, & Knopp, RH 2002, “Gestational diabetes and the incidence of type 2 diabetes: a systematic review ”, Diabetes Care , vol. 25, pp. 1862-68.

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Lee, AJ, Hiscock, RJ, Wein, P, Walker, SP & Permezel, M 2007, “Gestational diabetes mellitus: clinical predictors and long-term risk of developing Type 2 diabetes”, Diabetes Care, vol. 30, no. 4, pp. 878–883.

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Retnakaran, R & Baiju, RS 2009, “Mild glucose intolerance in pregnancy and risk of cardiovascular disease: a population-based cohort study”, Canadian Medical Association Journal, vol. 181, no. 6–7, pp. 371–377.

State Government of Victoria 2012, Diabetes-Gestational, Web.

Stone, CA, McLachlan, KA, Halliday, JL, Wein, P &Tippett, C 2002, “Gestational diabetes in Victoria in 1996: incidence, risk factors and outcomes ”, Medical Journal of Australia, vol.177, pp. 486–491.

Suhonen, L & Teramo, K 1993, “Hypertension and pre-eclampsia in women with gestational glucose intolerance”, Acta Obstetricia et Gynecologica Scandinavica, vol. 72, no. 4, pp. 269–272.

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• Endocrinology: Regulation of Growth Hormone
• Type 2 Diabetic - Suffering, New Pandemic
• Gestational Diabetes: American Diabetes Association Publishers
• Pathophysiology of Mellitus and Insipidus Diabetes
• Critical Analysis of Policy for Type 2 Diabetes Mellitus in Australia
• Blood Disorder: Disease Analysis
• The Endocrine System Researching
• Endocrine Disruption Through Phthalates/Plasticizers
• 52-Year-Old Female Patient With Type II Diabetes
• R-insulin: Article Critique
• Chicago (A-D)
• Chicago (N-B)

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Bibliography

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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

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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.

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 ).

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 ).

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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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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This study aimed to assess the need for practice-wide quality improvement to support evidence-based type 2 diabetes screening for women with a history of gestational diabetes mellitus (GDM) receiving primary care. We sought to add the diagnosis of GDM to the problem list of women who did not have it at baseline.

We identified all women in our practice with a history of GDM diagnosed between 2002 and 2023, quantified the proportion with GDM documented in their problem list, and examined patient- and clinician-level factors associated with having GDM appropriately documented at baseline.

We identified 203 women with GDM receiving primary care within internal medicine. Of the 203 women, 73 (35.0%) did not have GDM documented in their problem list. Of those without GDM included on the problem list, 52% were overdue for type 2 diabetes screening compared with 41% of those with GDM documented before our intervention. We found race, parity, and previous abnormal glycemic laboratory test results to be highly predictive of whether the history of GDM was on patients’ problem list. Upon completion of our intervention, we successfully achieved a 100% documentation rate for GDM diagnosis for women who previously lacked documentation in their problem list.

This work paves the way for targeted interventions aimed at improving care for women with a history of GDM, including delivery of interventions and education to prevent the onset of an appropriate clinical screening for type 2 diabetes.

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REVIEW article

Implementing care for women with gestational diabetes after delivery—the challenges ahead.

• 1 Department of Endocrinology, National University Health Systems, Singapore, Singapore
• 2 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
• 3 Department of Digestion, Metabolism and Reproduction, Imperial College London, London, United Kingdom

Gestational diabetes (GDM), defined as glucose intolerance during pregnancy, affects one in six pregnancies globally and significantly increases a woman’s lifetime risk of type 2 diabetes mellitus (T2DM). Being a relatively young group, women with GDM are also at higher risk of developing diabetes related complications (e.g., cardiovascular disease, non-alcoholic fatty liver disease) later in life. Children of women with GDM are also likely to develop GDM and this perpetuates a cycle of diabetes, escalating our current pandemic of metabolic disease. The global prevalence of GDM has now risen by more than 30% over the last two decades, making it an emerging public health concern. Antepartum management of maternal glucose is unable to fully mitigate the associated lifetime cardiometabolic risk. Thus, efforts may need to focus on improving care for women with GDM during the postpartum period where prevention or therapeutic strategies could be implemented to attenuate progression of GDM to DM and its associated vascular complications. However, strategies to provide care for women in the postpartum period often showed disappointing results. This has led to a missed opportunity to halt the progression of impaired glucose tolerance/impaired fasting glucose to DM in women with GDM. In this review, we examined the challenges in the management of women with GDM after delivery and considered how each of these challenges are defined and could present as a gap in translating evidence to clinical care. We highlighted challenges related to postpartum surveillance, postpartum glucose testing strategies, postpartum risk factor modification, and problems encountered in engagement of patients/providers to implement interventions strategies in women with GDM after delivery. We reasoned that a multisystem approach is needed to address these challenges and to retard progression to DM and cardiovascular disease (CVD) in women with GDM pregnancies. This is very much needed to pave way for an improved, precise, culturally sensitive and wholistic care for women with GDM.

1 Introduction

Gestational diabetes (GDM), defined as glucose intolerance during pregnancy, has risen in prevalence by more than 30% across all population groups over the last two decades, giving rise to an emerging public health burden ( 1 ). Globally, GDM is known to affect one in six pregnancies, with higher prevalence in Middle East and North Africa (30.2%) and in South-east Asia (23.7%) ( 1 ) ( Figure 1 ).

Figure 1. Prevalence (%) of gestational diabetes (GDM) worldwide (data from international diabetes federation atlas 2021). Created with Biorender.

Compared to women without GDM, women with GDM have a ten-fold increased risk of developing type 2 diabetes (T2DM) after the index pregnancy ( 2 ). In women with GDM, the linear risk of progression to diabetes is 9.6% per year after delivery, with the risk being higher in the first 5 years after delivery ( 2 , 3 ). Ethnicity modifies diabetes risk in different ethnic groups. Women of South Asians and Black ethnicity are associated with an increased absolute risk of T2DM compared to White ( 4 ). However, the relative incremental risk of progression from GDM to T2DM could actually be higher in White ethnic groups compared to women of Chinese and South Asian ethnicity (White: adjusted HR13.6; 95% CI 13.2,14.0), Chinese: adjusted HR9.2; 95% CI 8.1, 10.3; South Asian women: adjusted HR9.6; 95% CI 8.8, 10.5) ( 5 ). Additionally, women with GDM, despite being a relatively young cohort, have a two-fold increased risk of cardiovascular disease (CVD) ( 6 ) and non-alcoholic fatty liver disease (NAFLD) ( 7 ) after delivery. Children from women with GDM are more likely to be macrosomic at birth and have a greater propensity to develop obesity and T2DM later in life ( 8 ). Female offsprings are also likely to experience GDM in their own pregnancies resulting in a vicious intergenerational cycle of GDM ( 9 ).

Given that T2DM, CVD and NAFLD are significant sequels to GDM, close monitoring of postpartum GDM is essential to prevent the development of T2DM. This is because detection of dysglycaemia early in the trajectory of cardiometabolic disease could enable implementation of risk-modifying intervention that reduce the growing prevalence of diabetes ( Figure 2 ) but also mitigate associated cardiometabolic complications. However, an optimal cost-effective program to identify, monitor and manage women with GDM with elevated cardiometabolic risk post-delivery is currently lacking. In this review, we aim to summarize the key challenges in managing the metabolic sequalae in women with GDM during the postpartum period.

Figure 2. Long term cardiometabolic consequences of women with gestational diabetes mellitus. Created with Biorender.

2 Current challenges in postpartum management of women with GDM

2.1 challenges in postpartum testing, 2.1.1 is ogtt sufficient in stratifying glycaemic status postpartum.

The World Health Organisation (WHO) recommends a 75-grams oral glucose tolerance test (OGTT) as the screening test to reclassify glycaemic status in women with GDM after delivery ( 10 ). The OGTT involves a fasting glucose and a 2 h post-glucose load measurement and uses non-pregnancy criteria to identify women with impaired fasting glucose (IFG), impaired glucose tolerance (IGT), diabetes mellitus (DM), or normoglycaemia in the first 6 months after delivery ( 10 ). The IGT represents an intermediate state between normal and overt diabetes and individuals with IGT typically convert to T2DM at a rate of ∼5%–10% per year ( 11 , 12 ). However, the risk of dysglycaemia could extend into women with normal glucose tolerance (NGT); 17.1% of women with GDM with NGT at 3 months postpartum developed prediabetes/diabetes within a year after delivery ( 13 ). Women with NGT who progressed to prediabetes/diabetes have higher fasting, 1 h and 2 h glucose level and tend to have a delayed peak blood glucose level at 60 min (16.1% of the progressors peak at 60 min on an OGTT compared to 6.5% of the progressors who peak at 30 min) ( 13 ). Conceivably, the defects in insulin secretion are likely to be a continuous process that begins long before the onset of overt diabetes. A ∼40%–50% loss in β-cell function is expected in women who had NGT with a 2 h OGTT of 6.6 mmol/L to 7.8 mmol/L (120–140 mg/dl) ( 14 ). Ravi Retnakaran et al. observed that women with mild glucose intolerance during pregnancy that do not meet criteria for diagnosis of GDM had β-cell dysfunction at 3–12 months postpartum ( 13 , 15 , 16 ), suggesting a progressive loss of β-cell function beyond pregnancy. Loss of β-cell function is likely to be independent of changes in adiposity or insulin sensitivity ( 16 ), highlighting a key pathophysiologic process that drives dysglycaemia ( 13 , 17 , 18 ) in women with GDM after delivery.

Most guidelines have recommended repeating OGTT in 1-year after delivery to re-stratify diabetes risk ( 19 – 21 ). Longitudinal studies consistently reported increased CVD and T2DM risk in women with NGT ( 6 , 13 ) after delivery, thus a single 2 h OGTT measurement at 6–12 weeks postpartum may not have the sensitivity to identify women who are at high-risk for metabolic disease ( 22 ). Furthermore, OGTT is cumbersome, requires overnight fasting and additional staffing.

Abnormal glucose challenge test following an antepartum OGTT has been shown to predict pre-diabetes at 3 months postpartum with an AuROC of 0.754 in women with GDM compared to women with NGT during an antepartum OGTT ( 15 ). The glucose excursion during antepartum OGTT is a far more predictive metabolic marker compared to other metabolic measures such as the insulinogenic index or the homeostatic model assessment of insulin resistance (HOMA-IR) ( 15 ). Indeed, the number of abnormal OGTT values on a three-point OGTT test during pregnancy predicts the risk of developing T2DM at 5 years after the index pregnancy in a dose-response manner ( 23 ). A high fasting glucose during OGTT in pregnancy is strongly associated with development of T2DM in women with GDM compared to a high 2 h post-glucose load level ( 23 ). If glucose excursion values during pregnancy could provide insight into the future maternal risk of prediabetes ( 15 ), it would be reasonable to utilize it as a means to identify women at high-risk of glycemic and cardiometabolic deterioration in the postpartum period. This might be far more feasible especially when women rarely return for a postpartum OGTT test ( 24 , 25 ) (described in sections below).

2.1.2 Using 1-hour-post glucose level to predict diabetes and complications?

The 1 h plasma glucose level ≥8.6 nmol/L (155 mg/dl) during an OGTT may identify individuals with NGT at high risk of progressing to T2DM and CVD ( 26 – 28 ). A cohort study of 1945 non-diabetic men and women followed over 24 years showed that individuals with a 1 h prandial glucose of ≥8.6 mmol/L and a 2 h post-glucose level of <7.8 mmol/L had a 4.35-odds (95% CI 2.50−7.73) and a 1.87-odds (95% CI 1.09−3.26) of developing diabetes and prediabetes respectively ( 29 ). Elevated 1 h post glucose level of 8.6 mmol/L was also associated with an adverse cardiovascular risk profile characterised by higher blood pressure, elevated low-density lipoprotein, triglycerides and increased inflammatory markers and carotid intima thickness ( 30 – 32 ). In addition to macrovascular complications, 1 h plasma glucose of ≥8.6 mmol/L also predicted progression to microvascular complications, such as diabetic retinopathy and peripheral vascular complications, in individuals with NGT and IGT during 39 years follow-up ( 33 ). Compared to the 2 h post-glucose level, the 1 h post-glucose level of ≥8.6 mmol/L offered greater sensitivity in identifying a high-risk NGT group at an earlier time point before β-cell decline ( 22 , 29 , 33 ) in multiethnic groups ( 34 – 37 ) and predicted future diabetes better than fasting plasma glucose (FPG), 2 h plasma glucose, and HbA1c (AuROC of for 1 h plasma glucose of 0.84; AuROC for FPG 0.75; AuROC of 2 h plasma glucose is 0.79 and AuROC of HbA1c is 0.73) ( 27 , 28 , 38 , 39 ).

The utility of 1 h post glucose value was endorsed by International Diabetes Federation (IDF) ( 40 ). In a recent position statement, individuals with 1 h post-glucose value of ≥8.6 mol/L were categorized as intermediate hyperglycaemia and should be commenced on lifestyle prevention program ( 40 ). People with 1 h post glucose level of ≥11.6 mmol/L were classified as T2DM and should have a repeat OGTT to confirm diagnosis ( 40 ). Overall, the accrued data suggested better stratification of risk of future T2DM, diabetes-related complications, and NAFLD with the 1 h post-glucose level of 8.6 nmol/L ( 40 , 41 ). This would be of great relevance to women with GDM who are likely to have an underlying mild β-cell defect, which may not become apparent until years after pregnancy ( 20 ). The shortened OGTT procedure (from 2 h to 1 h) is also more cost-effective and clinically appealing to women with GDM who found the 2 h OGTT procedure to be time-consuming ( 40 ).

2.1.3 Accuracy of other measures to assess glycaemic status in early postpartum period

Fasting plasma glucose (FPG) and HbA1c have been suggested as alternative screening tests to determine if a woman’s glucose status had returned to normal after delivery. FPG was correlated to HbA1c ( r  = 0.39) and the 2 h post-glucose value ( r  = 0.34) ( 42 ) but using FPG alone (at ≥6.1 mmol/L) resulted in missed diagnosis of impaired glucose tolerance (IGT) in 54% of women with GDM after delivery ( 43 ). In another study, 38.3% of women classified as glucose intolerance using OGTT test were reclassified as normal with a FPG ( 44 ). A postpartum FPG alone, whilst useful, may not be sensitive enough to ascertain glucose tolerance in high-risk multi-ethnic population ( 43 ), and is likely to lead to missed cases of diabetes and IGT.

Unlike FPG, HbA1c is relatively easy to perform but it could be affected by age, race, haematological factors or iron deficiency ( 45 – 48 ). HbA1c is not reliable in the first 1 year postpartum, due to blood loss during labour and persistence of high red cell turnover state ( 49 ). A HbA1c cut-off of 6.5% would misclassify 75% of the women with GDM who were previously categorized as abnormal glucose regulation by an OGTT test in the postpartum period ( 44 ). HbA1c is also weakly correlated with glycaemic parameters such as insulin sensitivity ( r  = −0.25, p  = 0.010) or glucose disposition index ( r  = −0.26, p  = 0.007) in women with GDM during early post-partum (3–6 months) ( 50 ). Using a lower HbA1c cutoff of ≥6% (42 mmol/mol) would increase the number of false negative that does not sufficiently identify IFG or IGT in postpartum GDM women (Specificity: 83.9%, 95% CI 73.2–92.9; Sensitivity: 23.8%, 95% CI 9.5–42.9) ( 50 ). Further lowering of the HbA1c cut-off to 5.7% would reduce its specificity ( 50 ). Notably, HbA1c 5.7−6.4% was a less precise predictor of glucose abnormalities in at risk individuals or in women with GDM in early postpartum period ( 42 , 50 ) but could inform progression of glucose intolerance if assessed longitudinally and periodically during postpartum period ( 50 ). FPG could be used in combination with HbA1c in the prediction of diabetes during the postpartum period ( 51 ). A study from India showed that a FPG of ≥6.1 mmol/L or HBA1c ≥ 6.0% avoided OGTT in 80.9% of the women, without missing any cases of diabetes compared to missing 2.4% cases of diabetes when either FPG ≥5.6 mmol/L or HbA1c ≥ 5.7% were used alone ( 51 ).

2.1.4 Lack of consensus in the guidelines on postpartum follow-up

Guidelines differ in terms of timing and the type of screening test for postpartum glycaemic status in women with GDM ( Table 1 ).

Table 1. Postpartum oral glucose tolerance test (OGTT) guidelines for women with a history of GDM.

The Australasian Diabetes in Pregnancy Society ( 52 ) and Endocrine Society ( 53 ) recommend screening for type 2 diabetes in women with previous GDM at least 6–12 weeks postpartum with a 75-g oral glucose tolerance test (OGTT), using non pregnancy criteria. The American Diabetes Association recommends screening for T2DM with an OGTT at an earlier time frame (4–12 weeks after delivery) to enable discussion of result at the 6-week postpartum obstetrical assessment ( 49 , 54 ), whereas the Canadian Diabetes Association (CDA) suggests the same test over a longer period of assessment (from 6 weeks to 6 months) ( 19 ). The American Congress of Obstetrician and Gynaecologist indicates screening with either the OGTT or testing with fasting plasma glucose (FPG) at 6–12 weeks postpartum ( 21 ). On the other hand, the National Institute of Health and care Excellence (NICE) excludes a routine OGTT and suggests testing with a FPG or HbA1c at 6–13 weeks postpartum if FPG is not done earlier at discharge ( 55 ). The substantial variation in clinical recommendations throughout the world has made it challenging to understand the trajectory of cardiovascular and metabolic risk of women with GDM after pregnancy.

2.2 Challenges in adherence to postpartum testing

2.2.1 adherence to post-partum ogtt (patient and provider’s perspective).

Despite the clinical relevance of OGTT in classifying postpartum dysglycaemia, uptake of postpartum OGTT has been universally low globally, ranging from 31%–49% in most studies ( 56 – 58 ). This is much lower compared to postnatal cervical screening (94%) and antenatal GDM screening (98%) ( 59 ).

Both patients and providers have highlighted several barriers to postpartum OGTT. Bennett et al. conducted a semi-structured interviews in women with GDM and identified several themes of barriers to postpartum OGTT testing, which include: (1) emotional stress of prioritizing newborn’s needs before a woman’s postpartum care needs, the challenging adjustment to the new role as a mother and fear of receiving a diagnosis of diabetes, (2) lack of communication from providers resulting in underappreciation of the condition and a perceived sense of lack of continuity of care due to change of healthcare providers ( 60 – 62 ). Interestingly, the barriers reported were largely congruent across different ethnic groups ( 61 , 63 ). Hewage SS et al. conducted an exploratory study in Singapore and found that despite universal GDM education, 37% of the women with GDM did not feel that postpartum OGTT was very important ( 61 ). The time-consuming nature of the OGTT test, the unpleasant taste of the glucose drink, inadequate education on postnatal care and lack of communication from relevant healthcare providers were highlighted as common barriers to postpartum OGTT amongst women with GDM in Singapore ( 61 ). Similarly, women with GDM of Hispanic, African American and White ethnic group would not adopt behaviour change before a subsequent pregnancy because they did not view prevention of GDM in future pregnancy as a priori ty ( 63 ). Although GDM was often seen as an important “wake-up” call for action, healthy behaviour change after pregnancy was typically not sustained ( 61 , 63 ) could also influence motivation for sustained behaviour change. In Singapore, cultural practices such as confinement diet [diet consisting of red date tea (high sugar content) and herbal soups] for 14–40 days after delivery resulted in women consuming more refined carbohydrates and indulging in cravings after confinement period ( 64 ). Thus, addressing the perceived beliefs regarding continuation of health behaviours after childbirth is crucial in a successful postpartum program ( 65 ) ( Table 2 ).

Table 2. Challenges in the management of women with gestational diabetes after delivery.

From the healthcare providers’ perspective, challenges in postpartum OGTT include lack of familiarity of screening protocols, attitudinal barriers such as having patients underestimating the severity of T2DM and perceiving the postpartum OGTT as unnecessary or costly ( 66 ). Even more worryingly, a study reported 49% of the incomplete OGTT was attributed to providers not requesting the test ( 67 ) ( Table 2 ).

2.2.2 Uncertainty between primary and secondary care for postpartum screening

A challenge in the management of postpartum GDM is the lack of clear directions as to who should bear the responsibility of postpartum care for women. In some countries, the primary care providers ( 68 ) are expected to follow up women with GDM with a postpartum OGTT, whereas in other countries, internists are involved in the postpartum care for women with GDM ( 69 ). In practice, the type of tests to be used in assessment of glycaemic status after childbirth, frequency and duration of follow up deviated from national guidelines ( 70 ). Most specialists (73%) recommended long-term postpartum follow up but only 39% of primary care providers recalled women with GDM for diabetes screening ( 70 ).

Fragmentation of health services is a major barrier to postpartum screening ( 68 , 70 , 71 ). Hewage SS et al. pointed out that women were more likely to comply to T2DM preventive measures if recommended by healthcare providers ( 61 ). However, including a postpartum specialist clinician visit did not always result in higher rates of postpartum OGTT completion ( 56 ), particularly if women with GDM were not motivated to return for postpartum screening. Of the 81.1% of women who had postpartum clinician visit, 52% did not have a postpartum OGTT despite being arranged for them prior to presentation to a postpartum clinic ( 56 ). This suggests that the way the message was framed and delivered could influence a women’s decision to adhere to postpartum healthy behaviours ( 61 ).

In some countries, establishing a registry of women with previous GDM was expected to improve uptake of postpartum OGTT ( 72 ) but real-life data on the effectiveness of the GDM registry is not yet known. In Australia, the gestational diabetes registry had facilitated the process of sending automatic reminders for women with GDM to attend pre-booked postpartum OGTT screening, leading to a 9% increase in postpartum OGTT testing ( 73 ). Using a registry to recall women with GDM into primary care for postpartum screening was also shown to be effective, suggesting a potential utility of incorporating GDM register into family practice ( 74 ) ( Table 2 ).

2.2.3 Interventions to improve OGTT uptake may not be translatable in clinical practice

Various measures have been undertaken to overcome the barriers to postpartum OGTT testing. These include patient reminders in the form of postal ( 75 ), email or phone messages ( 76 ), verbal and written antepartum counselling, flexible appointment times, advanced order sets for glucose monitoring at 35 weeks pregnancy visit, educational modules to increase awareness amongst women regarding metabolic risk ( 77 ). Whilst all these measures show reasonable improvement in the uptake of postpartum OGTT in clinical studies, changes in postpartum OGTT screening rates in clinical practice outside the context of clinical studies were minimal ( 24 ). This suggests a gap in the translation of research to healthcare practice. Involvement of other healthcare professionals, such as nurses or case managers, seems to improve postpartum OGTT adherence ( 25 , 78 ). As seen in the Women in India with Gestational Diabetes Strategies (WINGS) project in India, it is possible to obtain a 95.8% (203/212) postpartum follow-up rate through sustained efforts by trained healthcare professionals to contact women ( 79 ). Aside from periodic reminders, strategies such as offering postpartum screening to women with GDM during child immunization visits and integrating GDM screening with national public health programs have also been suggested ( 80 ). An electronic self-administered capillary OGTT device was reported to have good user-applicability by untrained individuals in community and could be tested as a screening tool in women with GDM in future ( 81 ).

Mobile applications such as smartphones and mobile apps are utilized as practical tools to motivate women to return for their postpartum follow-up ( 82 ). Early studies on mobile application-based interventions showed promising results, but long-term effectiveness of mobile applications in postpartum GDM management is unclear ( 83 ). Much work is still needed to determine the effectiveness of mobile applications in engaging a broad audience with various levels of literacy and digital experience ( 83 ).

2.2.4 Is postpartum OGTT enough to evaluate other metabolic risks?

Dyslipidemia is a physiological response in pregnancy driven by secretion of steroid hormone (e.g., progesterone), increased hepatic synthesis of triglycerides, and reduced lipoprotein lipase activity in adipose tissue ( 84 ). The characteristic finding at the 12th week of gestation is an elevated maternal triglyceride (TG) level and a mild increase in low-density lipoproteins (LDLs) and high-density lipoproteins (HDLs) ( 84 ). Altered lipid levels at 3 months postpartum ( 85 ) rarely normalise within a year after delivery ( 86 – 88 ). Of note, one in six women with abnormal glucose tolerance had an abnormal lipid profile postpartum, and one in four women with NGT had dyslipidemia ( 89 ). Another study reported 43% of women with GDM who had normoglycaemia at 6 months postpartum had dyslipidaemia ( 90 ). Dyslipidemia during and after pregnancy ( 88 ) aggravated endothelial dysfunction and promoted premature atherosclerosis ( 91 ), leading to increased CVD events per 10,000 person-years in women with GDM compared to those without (5.8 vs. 2.5, p  < 0.0001) ( 88 ). CVD events could occur in a subset of women with GDM who did not develop intercurrent T2DM (3.2 vs. 2.2, p  < 0.0001) ( 92 ). In these women, mediation analysis showed that HDL, triglycerides and LDL cholesterol (without glycaemia) contributed to elevated CVD risk at 40.8% 12.1% and 9.9%, respectively ( 92 ).

CVD monitoring and modification of CVD risk are thus critically needed in women with GDM after pregnancy. However, surveillance protocols for CVD have been mostly focused on individuals aged 40–80 years with T2DM and not on younger women with GDM ( 93 ). Females of reproductive age are less likely to be offered statin, and even if offered, they are less likely to comply ( 94 ). Therefore, future research should consider intervention strategies to reduce progression of atherosclerotic disease in women with GDM, beyond preserving the β-cell function.

2.3 Challenges in implementing postpartum interventions

2.3.1 decision on the most appropriate postpartum intervention.

Currently, the most appropriate lifestyle intervention to prevent diabetes during postpartum period is not known. The Diabetes Prevention Program (DPP) and the Finnish Diabetes Prevention Study (FDPS) have shown that lifestyle interventions were effective in reducing risk of T2DM by ∼58% in women with a history of GDM ( 95 , 96 ) and in at risk non-pregnant individuals ( 97 ). However, other lifestyle intervention trials during pregnancy did not show changes in fasting glucose or insulin sensitivity ( 98 , 99 ). Women enrolled in the Tianjin Gestational Diabetes Mellitus Prevention Program, had significant weight loss and reduction in plasma insulin levels in the lifestyle intervention arm compared to the control group during the first year ( 100 ) but it is unclear if these effects were sustained ( 101 ). A systematic review on lifestyle intervention conducted in at-risk population in lower-middle income countries (LMIC) showed a possible reduction in T2DM incidence by 25% but the type of lifestyle intervention was heterogenous ( 102 ).

Various factors could impact on the success of a diabetes prevention program. Besides the type of intervention (physical activity or dietary changes or both), the level of intensity of contact between the healthcare worker and women, the mode of contact and whether the trial design included patients with prior education or elements of behavioural therapy such as goal setting, stimulus control and motivational interview could influence outcomes. Participants in the DPP received 16-sessions (6 months) of intensive curriculum on behavioral change ( 103 ) to reach a 58% reduction in diabetes risk ( 95 ). In the Mothers After Gestational Diabetes in Australia Diabetes Program, a 12-months intervention consisting of program handbook, face-to-face and telephone follow up calls ensured participants achieve their health goals ( 104 ). Latino women with GDM received an 8-weeks culturally appropriate education classes and monthly support sessions over a 6-months period to sustain health behaviour change ( 105 ). In South Asian population (India, Sri Lanka and Bangladesh), a 12-months lifestyle intervention trial on diet and physical activity did not yield any change in glycaemic status at 14 months in women with GDM ( 106 ). The South Asian ethnic group is likely to have a different trajectory for developing dysglycaemia during the postpartum period. Thus, a cultural and country specific approach is clearly needed to implement diabetes prevention care after delivery ( 106 ).

Cost-effectiveness is an important factor to consider in the implementation of prevention programs for women with GDM. Unfortunately, few studies studied the cost-effectiveness of T2DM prevention in women with GDM. Werbrouck et al. concluded that an OGTT every three years could potentially lead to the lowest cost per T2DM case detected ( 107 ) but the modelling studies done were 14–30 years ago (1993–2010) and did not include incremental analysis or a comparator population of “no screening/prevention” ( 107 ). No further randomized controlled trials on the cost-effectiveness of lifestyle intervention programs has since been conducted ( 108 ), representing a clear research gap in women’s health.

Metformin and Troglitazone were studied as potential agents to reduce the risk of diabetes in women with previous GDM. Compared to the placebo, women with previous GDM ( n  = 350) benefited from metformin and intensive lifestyle modification, with both these interventions achieving a ∼50% and ∼53% risk reduction of diabetes, respectively ( 95 ). The effect of metformin or lifestyle intervention also persisted for 15 years in DPP study ( 109 ). Likewise, in the Troglitazone in Prevention of Diabetes (TIRPOD) study, treatment with Troglitazone (400 mg per day) in 133 women with GDM of Hispanic origin for 30 months resulted in more than 50% reduction in the incidence rate of T2DM (12.1% in Troglitazone vs. 5.4% in placebo group, P  = 0.03) ( 110 ). Two-thirds of the women receiving Troglitazone had improved insulin sensitivity and a greater mean decrease in fasting glucose ( 110 ) and protection against diabetes for 8 months after stopping therapy ( 110 ). Due to concerns about hepatotoxicity, troglitazone was discontinued. Dipeptidyl-peptidase IV (DPPIV) inhibitors and sodium-glucose co-transporter 2 (SGLT2) inhibitors were studied in small number of patients with previous GDM. A proof-of-concept study in forty women with prior GDM showed that a 16-weeks treatment with metformin and sitagliptin significantly increased first-phase insulin secretion from 720.3 ± 299.0 to 995.5 ± 370.3 pmol/L ( P  = 0.02) but no significant change was observed with sitagliptin or metformin alone ( 111 ). In another study, women with previous GDM lost 4.9% of their original weight after 24 months of dapagliflozin-metformin combination compared to metformin (1.4% weight loss) or dapagliflozin alone (3.2%) ( 111 ). Women with prior GDM randomized to 84-weeks of metformin 2000 mg and liraglutide 1.8 mg subcutaneously per day had improved postpartum insulin sensitivity and reduced body weight compared to women receiving metformin alone ( 112 ). More studies are clearly needed to establish the optimal early postpartum treatment for this high-risk young cohort.

2.3.2 Implementation of care in high-income (HIC) and low middle-income (LMIC) countries

Challenges faced in implementing postpartum GDM care are contextual and highly dependent on the societal/cultural barriers and health system resources available for maternal care in each country. Postpartum care for women with GDM in high income countries (HIC) is at present, suboptimal ( 66 ). On an individual level, the barriers identified in HIC include fear of diagnosis of diabetes, inadequate information on postpartum care, difficulties in adhering to a healthy lifestyle long term ( 60 , 113 – 117 ). From a health system perspective ( 60 ), challenges perceived are lack of concern on postpartum health by policy makers ( 67 ), lack of agreed quality and accountability measures between providers and patients on a global/local level ( 66 , 118 ). Most countries by default, would refer women with GDM to primary care as a standard practice but quality of postpartum care in each practice varies ( 20 , 21 , 119 ). In Finland, a universal healthcare system exists to provide a series of intervention from primary care to preventive care and through to treatment for women with GDM ( 120 ). However, even in Finland, return rate for postpartum OGTT testing ranges from 30.9%–85.2%, with higher rates of return in areas that offer lifestyle intervention ( 121 ). In the United States, continuous care to pregnant women with or without GDM during the postpartum period depends on whether the women were enrolled in health systems that offer prevention programs ( 122 ). In Australia, postpartum care depends on whether the woman is followed up in a public or private sector ( 123 ). Those receiving postpartum OGTT test in a public sector are likely to have fragmented care due to inadequate staffing, difficulty in establishing a continuity of care after delivery ( 123 ) while those in private sectors are more likely to be enrolled in a long-term follow up programme ( 123 ).

The data on postpartum care for women with GDM in LMIC are limited, compared to HIC ( 66 , 118 , 124 ). Some of the challenges identified in LMIC are similar to those seen in HIC (e.g., fear/anxiety about the perceived diagnosis of overt diabetes) ( 125 , 126 ). However, the more pertinent issues are associated with social and cultural issues and differences in health systems between countries ( 60 , 118 , 124 , 127 ). Shortage of trained healthcare professionals ( 118 ), issues with transportation to health centres ( 128 ) or lack of financial means to see a healthcare professional and poor understanding on implication of GDM on long term metabolic health ( 125 , 129 ) are highlighted as barriers to postpartum follow up ( 127 ). The lack of robust follow-up systems ( 124 ), guidelines or glucose equipment for postpartum care ( 118 ) pose substantial barriers to screening and counselling. Healthcare professionals in LMIC such as India or Turkey often do not recommend women with GDM to have postpartum testing according to latest evidence ( 130 , 131 ). It is therefore not surprising that fewer than one in ten people with diabetes in LMIC receive the standard level of care as detailed in international guidelines ( 132 ). In LMIC, inadequate collaboration between different specialists impairs the process of coordinating care for women ( 124 ). Women often have to consult different services and specialists and the delays experienced in receiving care increases the risk of drop-outs ( 124 ).

Society and cultural factors influence the provision of care. In Southeast China ( 133 ) or Vietnam ( 134 ), GDM is perceived by women or family members as an insignificant condition that disappears after delivery and this greatly influence their care-seeking behaviour ( 134 ). Husbands’ approvals are sometimes needed before a woman seek for medical care ( 124 ). Illiteracy and the cultural expectation for woman to deliver at home results in missed opportunities to educate women and family ( 118 ). In Tonga, physical activity as a preventive measure is perceived as a “foreign” concept, resulting in a reluctance to engage in physical activity measures after delivery ( 135 ). Although society and cultural issues emerge as a prevailing factor in shaping care in LMIC, factors such as low perceived importance of postpartum GDM care by policy makers ( 66 , 67 , 118 , 127 ), absence of financing strategies and disorganized care processes remain a common issue globally ( 129 , 133 , 134 ). Despite these issues, delivery of postpartum care is still possible if innovative, country-specific and culturally appropriate methods are carried out (see below) ( 58 , 79 , 136 , 137 ).

Medical specialisation has continued to expand in LMIC but the type and number of specialists available to deliver care in a particular field may not necessarily translate to improved service availability ( 138 ). Factors like inadequate incentivisation and career advancement opportunities for specialists in public sector often lead to migration of specialists from public to private sector, which influence delivery of equitable public health services ( 138 ). Thus, country-specific policies should be in place to determine the level of health systems that require specialists’ involvement ( 138 ). Public health services data in Iran and China showed that community health workers could play a beneficial role in coaching, hypertension and diabetes prevention ( 139 , 140 ). In Nepal and India, early preliminary studies suggest that mobile or tablet-based electronic decision support systems led by health workers could support patient education and improve screening and management of GDM ( 141 , 142 ). A good example of success is the Women in India with GDM Strategy Project (WINGS) in Southern India ( 136 ) which showed improved GDM complications rate ( 79 ), postpartum follow up and a reversal of trend of declining physical activity associated with pregnancy with low-cost intervention n ( 137 ). Innovative measures used include having trained health workers educate on nutrition through cooking demonstrations ( 130 , 136 ) or via a diet and nutrition “snakes and ladders” game ( 136 ), providing women with GDM a nutrition booklet ( 136 ) and a pedometer to increase daily step count ( 137 ), and contacting women to remind them to return for postpartum follow up ( 79 ).

3 Conclusion

Management of women with GDM has conventionally been focused on lowering the glycaemic excursion during pregnancy with the overarching aim of reducing pregnancy complications and fetal macrosomia. However, evidence suggests life-long metabolic sequalae of GDM impacts on a woman’s overall health ( 6 , 8 ), and with this, the larger social construct. Despite this, care for women with GDM in the postpartum period is suboptimal. A seamless transition from obstetric care to primary care with an emphasis metabolic and cardiovascular health in women with GDM is currently non-existent. Thus, it is critical to recognize GDM as a double-edge sword, which presents as a risk to mother and child during antenatal period but also an opportunity to modify the progression to overt T2DM and CVD ( 143 ). This needs to occur in tandem with efforts from clinicians, policy makers and professional bodies. Whilst novel and emerging anti-diabetic medications could offer promise, this risk is unlikely to be fully mitigated if efforts are not made to engage, educate and empower these “high-risk” women. A system level change is required to facilitate transfer of medical information between healthcare professionals and community, and this should occur in parallel with social support programs that promote lifestyle intervention to promote a global shift in healthcare beliefs and practice. Women with previous GDM are in the most productive years of their lives, not limiting to economy contribution and family building. Evidently, an orchestrated program of care amongst different specialists and various domains is urgently needed to improve women’s health. There is clearly much work to be done before we could bridge evidence into clinical practice but overcoming the obstacles ahead is a necessary step to realise a future of diminished diabetes risk in women with GDM and their future generations.

Author contributions

PE: Conceptualization, Supervision, Writing – original draft, Writing – review & editing. AT: Writing – review & editing. TY: Writing – review & editing. CK: Supervision, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article.

PCE is funded by NUS ExxonMobil and NMRC (National Medical Research Council) New Investigator Grant. The views expressed are those of the authors and not necessarily those of the abovementioned funders.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

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Keywords: gestational diabetes, postpartum, cardiovascular disease, impaired glucose tolerance, oral glucose tolerance test (OGTT)

Citation: Eng PC, Teo AED, Yew TW and Khoo CM (2024) Implementing care for women with gestational diabetes after delivery—the challenges ahead. Front. Glob. Womens Health 5 :1391213. doi: 10.3389/fgwh.2024.1391213

Received: 25 February 2024; Accepted: 31 July 2024; Published: 16 August 2024.

Reviewed by:

Copyright: © 2024 Eng, Teo, Yew and Khoo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Pei Chia Eng, [email protected]

† ORCID: Pei Chia Eng orcid.org/0000-0002-4172-1344 Ada Ee Der Teo orcid.org/0000-0002-8832-0409 Tong Wei Yew orcid.org/0000-0002-7349-3841 Chin Meng Khoo orcid.org/0000-0003-1601-2391

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Diet and Healthy Lifestyle in the Management of Gestational Diabetes Mellitus

Louise rasmussen.

1 Department of Obstetrics and Gynaecology, Aarhus University Hospital, Palle Juul-Jensens Boulevard 99, 8200 Aarhus N, Denmark; kd.mr@7aruol (L.R.); kd.mr@luoprahc (C.W.P.); [email protected] (P.G.O.)

Charlotte Wolff Poulsen

Ulla kampmann.

2 Steno Diabetes Center Aarhus, Aarhus University Hospital, Hedeager 3, 8200 Aarhus N, Denmark; kd.mr@tspoallu (U.K.); kd.mr@demsnits (S.B.S.)

Stine Bech Smedegaard

Per glud ovesen, jens fuglsang.

Gestational diabetes mellitus (GDM) among pregnant women increases the risk of both short-term and long-term complications, such as birth complications, babies large for gestational age (LGA), and type 2 diabetes in both mother and offspring. Lifestyle changes are essential in the management of GDM. In this review, we seek to provide an overview of the lifestyle changes which can be recommended in the management of GDM. The diet recommended for women with GDM should contain sufficient macronutrients and micronutrients to support the growth of the foetus and, at the same time, limit postprandial glucose excursions and encourage appropriate maternal gestational weight gain. Blood glucose excursions and hyperglycaemic episodes depend on carbohydrate-intake. Therefore, nutritional counselling should focus on the type, amount, and distribution of carbohydrates in the diet. Further, physical activity has beneficial effects on glucose and insulin levels and it can contribute to a better glycaemic control.

1. Introduction

Pregnant women gradually develop insulin resistance during pregnancy, thereby ensuring sufficient nutrient supply for the growing foetus [ 1 ]. In women with gestational diabetes mellitus (GDM), the insulin resistance leads to hyperglycaemia [ 2 , 3 ]. The definition of GDM is glucose intolerance with onset or first recognition during pregnancy [ 3 ]. Glucose passes through the placenta to the foetus and increases foetal insulin production, which, in turn, stimulates foetal growth, causing macrosomia and children large for gestational age (LGA) [ 4 ]. In the short-term, GDM is associated with increased risk of adverse pregnancy outcomes with a following long-term risk of childhood obesity and type 2 diabetes in mother and offspring [ 5 ]. The prevalence of GDM is rising [ 4 ], and so is the need for treatment.

Lifestyle changes are essential in the management of gestational diabetes. First-line treatment in GDM is medical nutrition therapy, together with weight management and physical activity [ 6 , 7 ]. It has been suggested that lifestyle modification alone is sufficient to control blood glucose in 70–85% of the women that were diagnosed with GDM [ 7 ]. How the diet should be composed for women with GDM is a complex matter and still not completely settled. In this review, we seek to provide an overview of the most important dietary interventions and components and how to treat and guide each woman with GDM during pregnancy.

2. Energy Requirements

2.1. optimal weight gain.

The recommended weight gain during pregnancy in women with GDM is the same when considering normal glucose tolerance pregnancies (NGTP). Gestational weight gain (GWG) should maintain the growth and development of the foetus [ 8 ]. The weight recommendations vary slightly from country-to-country. However, many countries refer to the recommendations for GWG that were made in 1990 by the Institute of Medicine (IOM) of National Academies, which were updated in 2009 based on pre-pregnancy Body Mass Index (BMI) (See Table 1 ) [ 8 ].

Recommendations for total weight gain during singleton pregnancy.

Modified from Table S1 in the IOM report by Rasmussen & Yaktine, “Weight Gain During Pregnancy: Reexamining the Guidelines (2009)” [ 8 ]. BMI, body mass index.

These weight gain guidelines are based on studies that indicated that women, whose weight gains are outside the recommended ranges, are at increased risk of adverse maternal and neonatal outcomes, such as pregnancy complications, maternal postpartum weight retention, and obesity, in the offspring [ 9 ].

In the guidance of pregnant women, a recommended rate of weight gain during 2nd and 3rd trimester can be helpful. Hence, women with a BMI of less than 18.5 kg/m 2 should be recommended a weight gain between 0.44–0.58 kg/week. Women with a BMI between 18.5 to 24.9 kg/m 2 should be recommended a weight gain between 0.35–0.50 kg/week. Women with a BMI between 25.0 to 29.9 kg/m 2 should be recommended a weight gain between 0.23–0.33 kg/week and, finally, women with a BMI of 30 kg/m 2 or above should be recommended a weight gain between 0.17–0.27 kg/week [ 8 ].

2.2. Energy Requirements for Normal or Underweight Women

There is not sufficient evidence to suggest that the energy requirements for women with GDM should be different from normoglycemic women or suggest a specific optimal calorie intake for women with GDM [ 10 ]. In the clinic, the energy expenditure can be calculated using the equation by Henry multiplied by a factor of physical activity level (PAL) or the equations that were recommended by the IOM (see Table 2 ).

Equations to calculate estimated energy requirement for nonpregnant women.

NNR, Nordic Nutrition Recommendations; IOM, Institute of Medicine; PA, physical activity coefficient; PAL, physical activity level; MJ, mega Joule; W, weight in kilograms; H, height in meters, d, day. Modified from the IOM report by Rasmussen & Yaktine 2009, “Weight Gain During Pregnancy: Reexamining the Guidelines” and The Nordic Council of Ministers 2014 “Nordic Nutrition Recommendations: Integrating nutrition and physical activity” [ 8 , 11 ].

The physical activity coefficient or level (PA/PAL) can be determined by the reference values that were given by the Nordic Nutrition Recommendations (NNR) or the IOM (see Table 3 ).

Physical activity level (PAL) for use in equations for energy requirement recommended by NNR and Physical Activity Coefficients (PA values) for use in equations for Energy requirement recommended by IOM.

IOM, Institute of Medicine; NNR, Nordic Nutrition Recommendations; PA, physical activity coefficient; PAL, physical activity level. Modified from Table 8.7 chapter 8 in the Nordic Council of Ministers 2014 guideline “Nordic Nutrition Recommendations: Integrating nutrition and physical activity” [ 8 ] and Table B-1C from the IOM report by Rasmussen & Yaktine, “Weight Gain During Pregnancy: Reexamining the Guidelines (2009)” [ 11 ].

An additional assessment of daily energy requirements during pregnancy is based on trimesters, although there is no international agreement on the exact calorie requirements during the three trimesters (see Table 4 ). There may be considerable variance in the total energy requirement among women with GDM as in NGTP [ 12 ], and each patient should be regularly weighed during pregnancy.

Additional daily calorie requirements during pregnancy.

IOM, Institute of Medicine; NNR, Nordic Nutrition Recommendations [ 8 , 11 ].

2.3. Energy Requirements for Women with Overweight or with Excessive Gestational Weight Gain

In women with GDM, excessive weight gain has been associated with an increased risk of hypertensive disorders of pregnancy, caesarean section, and LGA-babies [ 13 , 14 ]. Additionally, a meta-analysis concludes that it is extremely important to prevent excessive weight gain in GDM pregnancies [ 14 ].

In women with GDM, who have already accomplished a recommended weight gain, weight stabilization is the goal and calorie restriction can be necessary. In women with obesity and GDM, a 30–33% calorie restriction has been shown to reduce hyperglycaemia and plasma triglyceride levels [ 15 ]. In a retrospective cohort by Kurtzhals et al., the women with GDM who had the best dietary adherence to an energy-restricted “diabetes diet” and the lowest weight gain had lower foetal growth (infants with a birth weight-SD (standard deviation) score of 0.15 ± 1.1 in contrast to a birth weight-SD score of 0.59 ± 1.6) and decreased HbA1c, as compared to women with GDM with the highest GWG and poor dietary adherence [ 5 ].

2.4. Summary, Energy Requirements

The general recommendations for weight gain and the calculation of energy requirements for NGTP are also appropriate for women with GDM. Furthermore, particular attention should be given in order to avoid excessive weight gain. In women with obesity, or women who have already reached the recommended weight gain, a calorie restriction of 30–33% may be advisable.

3. Carbohydrates

In women with GDM, carbohydrates are the most important macronutrient. The digestion and absorption of carbohydrates cause an increase in blood glucose levels, and postprandial hyperglycaemia is primarily dependent on carbohydrate-intake [ 16 ]. The amount and the type of carbohydrate will both impact glucose levels [ 7 ]. Thus, a high intake of carbohydrate in a meal can result in hyperglycaemia [ 16 ]. However, glucose is the principal energy substrate for the placenta and foetus, which is essential for normal foetal growth and metabolism [ 17 ]. The IOM recommends 46–65 Energy percent (E%) from carbohydrates and a minimum of 175 g of carbohydrate daily to ensure appropriate foetal growth and cerebral development and function [ 2 , 8 , 10 ]. Ketonemia and/or ketonuria should be avoided, as it has been associated with lower mental or motor function in the offspring [ 2 ]. Carbohydrates should predominantly consist of starchy foods, a low glycaemic index, and a naturally high content of dietary fibre, such as vegetables, legumes, fruits, and whole grains [ 2 , 18 , 19 ]. The intake of added sugars should be kept low. The IOM has not set a daily intake of added sugars that individuals should aim for, but recommends that the intake of added sugar is limited to no more than 25% of total energy during pregnancy [ 8 ].

3.1. Low-Carbohydrate Diets

There is no international agreement on an appropriate amount of daily carbohydrate intake for women with GDM. Some guidelines recommend that the daily carbohydrate intake should not exceed 40–50E% [ 20 ]. Other countries, like Denmark, follow the general recommendation for NGTP, which, in the Nordic countries, is 45–60E% [ 11 ]. Only few clinical trials comparing low-carbohydrate diets with higher-carbohydrate diets have been conducted. Hernandez et al. compared a 40% carbohydrate diet with a 60% carbohydrate diet in a randomized crossover study. The 60% carbohydrate diet consisted of higher-complex carbohydrate. The low-carbohydrate diet resulted in a lower postprandial glucose, lower daytime mean glucose concentrations, lower area under the curve of 2 h postprandial glucose, and lower 24 h total glucose area under the curve, when compared with the 60% carbohydrate diet [ 21 ]. However, in the group receiving a 60% carbohydrate diet, the postprandial glucose values were still below current targets: 1 h <140 mg/dL (7.8 mmol/L) and 2 h <120 mg/dL (6.7 mmol/L). No differences for fasting blood glucose was found [ 21 ]. Moreno-Castilla et al. did not find any differences between groups in insulin treatment or in pregnancy outcomes, such as caesarean sections, LGA-babies, macrosomia, or gestational age at delivery, when comparing a 40% carbohydrate-diet with a 55% carbohydrate diet in a non-crossover randomized study [ 3 ]. Thus, there are conflicting results and it should be pointed out that a lower carbohydrate intake will often lead to an increased intake of fat, which, outside pregnancy, has been associated with an increase in serum fatty acids, insulin resistance, and increased foetal fat accretion and infant adiposity in NGTP [ 21 ].

3.2. Dietary Fibres

Normally, simple carbohydrates result in higher postprandial excursions than complex carbohydrates. NNR recommends a minimum of 25 g dietary fibre for women in general [ 11 ], while the American Diabetes Association recommends a minimum of 28 g of fibre to women with GDM [ 10 ], which is similar to IOM recommendations for normoglycemic women during pregnancy [ 8 ]. These recommendations can be met by eating 600 g of fruit and vegetables a day with a minimum of 300 g vegetables, with focus on rough and fibrous vegetables and by choosing wholemeal bread, pasta, and rice.

3.3. Low Glycaemic Index Diets

Carbohydrate food can be classified in relation to its effect on postprandial blood glucose expressed as a percentage of the blood glucose response of a reference food (e.g., glucose solution or white bread). The Glycaemic Index (GI) is a number from 0 to 100 that is assigned to a food, with pure glucose being arbitrarily assigned the value of 100, which represents the relative rise in the blood glucose level two hours after consumption [ 22 ].

Fast absorbable carbohydrates with a GI >70 are considered as high GI foods, while slowly absorbed carbohydrates with a GI ≤55 are considered low GI foods [ 22 ]. Moses et al. did show a reduced need for insulin in women with GDM, when they consumed a diet with a low GI in a RCT of 63 women with GDM. Even though Moses et al. compared with a diet high in fibre and a low sugar content, a lower GI diet significantly reduce insulin requirements in women with GDM [ 23 ]. In a meta-analysis of six RCTs and 532 women with GDM, Xu et al. found that a low-GI diet significantly reduced 2 h postprandial glucose concentrations, without any effect on fasting plasma glucose (FPG), birth weight, HbA1c, macrosomia, or insulin requirements [ 24 ]. Moreover, in a recent systematic Cochrane review that included 19 randomized trials and 1389 women with GDM, no effect of a low GI-diet on LGA or other primary neonatal outcomes was found [ 25 ].

In the case of GI, the amount of carbohydrate is not considered, which is also a strong factor in the prediction of the postprandial blood glucose response. Glycaemic load (GL), on the other hand, is the product of the total available carbohydrate content in a given amount of food and a given GI [ 22 ]. Low GL diet has been shown to improve glycaemic control in type 2 diabetes [ 26 ]. The results might also apply to GDM, as GDM and type 2 diabetes mellitus (T2DM) are both characterized by insulin resistance [ 27 ]. In a study by Bao et al. of healthy adults, the GL was a more powerful predictor of postprandial glycaemia and insulinemia when compared to the carbohydrate content [ 28 ]. In a recent study by Lv et al., 134 women with GDM were randomly allocated to either conventional nutritional nursing or specific nutritional nursing intervention based on GL. Significant differences in fasting blood glucose and the 2 h postprandial glucose levels between the two groups was found with lower levels in the group receiving intervention based on GL [ 29 ]. No statistically significant differences in the rates of adverse pregnancy outcomes, such as preterm delivery, foetal macrosomia, and foetal distress, was found; however, there was a lower incidence of premature delivery, eclampsia, pregnancy hypertension syndrome, and foetal macrosomia in the group receiving nutritional nursing based on GL [ 29 ].

3.4. Meal Frequency and Carbohydrate Distribution

A daily meal frequency of three main meals and 2–3 small meals or snacks is recommended to avoid excessive food intake at the same time, more specifically to avoid large quantities of carbohydrate and, thereby, reduce the postprandial blood glucose that is illustrated in Figure 1 [ 2 , 4 , 20 , 30 ].

The blood glucose levels according to different strategies for daily food intake. Blue curve illustrates the normal meal pattern and red curve illustrates meal pattern in women with gestational diabetes mellitus (GDM) to avoid excessive blood glucose fluctuations and to preserve the planned number of calories to be ingested. Blue arrows: Three main meals. Red arrows: three main meals and three snacks.

It has been suggested that breakfast should only contain small amounts of slowly absorbed carbohydrates, because there is usually a higher postprandial increase in blood glucose in the morning [ 20 ]; some guidelines recommend a maximum of 30 g carbohydrate at breakfast [ 30 ]. However, these recommendations are primarily based on personal experience and the scientific evidence is limited. In a randomized crossover study with 12 women with GDM, Rasmussen et al. demonstrated a significantly lower mean glucose and fasting blood glucose on a diet with a high carbohydrate intake in the morning as compared with a low carbohydrate intake in the morning. During both intervention periods (high and low carbohydrate in the morning), the recommended total carbohydrate intake was 46E% ± 2E%. In the same study, insulin resistance (as measured by homeostatic model assessment for insulin resistance (HOMA-IR)) significantly decreased during the period with the high carbohydrate intake in the morning. However, Rasmussen et al. also found a higher mean amplitude of glucose excursions and coefficient of variation in the group receiving a high carbohydrate intake in the morning as compared with the low intake [ 31 ]. There is a lack of randomized clinical trials studying whether a high or low carbohydrate intake in the morning is preferential.

3.5. Artificial Sweeteners

In the United States, the intake of artificial sweeteners (AS) during pregnancy has been increasing in recent years [ 32 ] and, in a study from Norway, it is reported that more than 40% of the pregnant women consumed artificially sweetened beverages (ASB) more frequently than once per week in early pregnancy [ 33 ]. It is conceivable that the intake of AS is particularly high in women with GDM, seeking to limit the intake of sugar and, to a greater extent, opt for “sugar-free” products and “No added sugar” products.

The Acceptable Daily Intake is defined as an estimate of the amount of food additive that can be ingested daily over a lifetime without health risk. The average use of AS, also called Non-Nutritive sweeteners (NNS), is usually below this limit and the US Food and Drug Administration and European Food Safety Authority, which regulates AS and NNS, has reported asulfame potassium, aspartame, saccharin, and steviol glycosides to be safe for use by the general public, including in pregnancy [ 34 , 35 ]. Observational human studies regarding AS and NNS exposure are often difficult to interpret because of heterogeneity and the lack of accuracy of self-reported intake of AS and NNS. In NGTP, some issues of concern, including increased infant BMI, childhood obesity, and small increase in preterm birth, have been observed [ 36 ]. Concerning preterm birth, the European Food Safety Authority has concluded that there is no evidence available to support a causal relationship between the consumption of ASBs and preterm delivery [ 37 ].

In a prospective study from the Danish National Birth Cohort, it was shown that approximately half of the women with GDM reported consuming ASB during pregnancy and 9% consumed it daily. When compared to no consumption, daily ASB intake during pregnancy was positively associated with an 1.57-fold increase in LGA risk in offspring, positively associated with an 0.59 SD increase in BMI z-scores at seven years and a 1.93-fold increased risk of overweight/obesity at seven years. The substitution of ASBs with water during pregnancy was associated with a 17% reduced risk for overweight/obesity at seven years, whereas sugar-sweetened beverages (SSB) substitution with ASBs was not related to a lower risk, but with an 1.14-fold increased risk of offspring overweight at seven years [ 38 ].

More studies, especially RCTs, on ASB and data with longer follow-up time are wanted.

3.6. Summary, Carbohydrates

Carbohydrate is the macronutrient that has the greatest impact on postprandial hyperglycaemia. Despite some studies suggesting a beneficial effect of low-carbohydrate diets, there is currently no evidence to recommend a carbohydrate intake that is lower than in NGTP and a minimum of 175 g of carbohydrate should be ensured. The exact amount of carbohydrate should be individualized, and the focus should be on the types of carbohydrate. Carbohydrates should predominantly consist of starchy foods with a naturally high content of dietary fibre, such as vegetables, legumes, fruits, and whole grains. Furthermore, carbohydrate intake should be distributed throughout the day in order to avoid excessive amounts that result in postprandial hyperglycaemia.

During pregnancy, there is an increased requirement of protein due to its role in the synthesis of maternal (blood, uterus, and breasts), foetal, and placental tissues [ 11 ]. The recommended amount of protein in the dietary treatment of GDM is similar to the general nutrition advice for normal pregnancies. The IOM recommends 10–35E% from protein during pregnancy, and an estimated average requirement of 0.88 g/kg/d with a minimum recommended daily intake of 71 g protein [ 8 ]. NNR recommends a protein intake of 10–20E% for non-pregnant adult women, corresponding to approximately 0.8–1.5 g protein/kg/d based on a PAL of 1.6 for an intake of 10E% and a PAL of 1.4, for an intake of about 20E%, respectively. Further, NNR recommends an additional safe intake of protein for healthy women during pregnancy gaining 13.8 kg of 0.7, 9.6, and 31.2 g/d during first, second, and third trimester, respectively [ 11 ]. In general, most pregnant women are able to cover their protein needs, as the increased requirement of protein is met by consuming a normal diet in a quantity that allows a weight gain within the recommended limits [ 11 ].

4.1. Protein Metabolism in GDM

The antepartum loss of nitrogen is lower than the postpartum loss, which suggests a reduction in protein catabolism to accrete more nitrogen to support maternal and foetal growth [ 39 ]. The loss of nitrogen is similar in GDM pregnancies and normal pregnancies [ 39 , 40 ]. In early GDM, when patients have less metabolic decompensation, there appears to be no difference in leucine kinetics/rate of protein turnover [ 41 ]. Later in gestation, when insulin resistance is more pronounced and antidiabetic treatment may be intensified with diet and sometimes insulin, the rate of protein turnover is increased in women with insulin treated GDM [ 40 ]. The increased protein breakdown, together with the normal urea excretion, suggests an increased pool of amino acids (AA) available to the placenta and thereby the foetus. The increased pool of AA in GDM and the association with macrosomia is unclear, as the results are often conflicting. One study found no correlation between AA and birth weight in GDM [ 40 ]; another found a correlation between leucine and birth weight for both GDM and NGTP controls [ 41 ].

4.2. Protein, the Placenta and GDM

A study in Chinese women with GDM found an inverse relationship between protein intake and placental size without any association with birth weight [ 42 ]. AA are carried across the placenta through an active transport system providing a greater concentration of AA in the foetus when compared to the mother [ 43 ]. In GDM, the transfer of AA across the placenta has been shown to be both decreased [ 44 ], unchanged [ 45 ], and increased [ 46 ]. A study showed elevated levels of branch chained amino acids (BCAA) in GDM as compared to pregnant women with normal glucose tolerance [ 47 ]. It has been suggested that the flux of insulinotropic AA (e.g., BCAA) over the placenta affects the beta cell of the foetus creating hyperinsulinemia affecting foetal growth [ 48 ]. Studies using metabolomics on cord blood, including both normal and GDM pregnancies, found no association between BCAA and increased insulin/c-peptide levels, thus not supporting BCAA as a cause of foetal hyperinsulinemia [ 49 , 50 ]. However, there was an association with birth weight, but not with the sum of skinfolds [ 49 ] or infants being LGA [ 50 ]. These findings suggest an association with lean body mass, but not with fat mass.

4.3. Plant vs. Animal Protein

Animal proteins are considered to be complete proteins, as they contain all nine essential AA while plant proteins are considered incomplete, as they can be deficient of one or more essential AA. However, a variety of plant based proteins consumed throughout the day provide sufficient essential AA [ 51 ]. A review including studies on vegetarian and vegan diets during pregnancies with sufficient energy and protein supply in the setting of no financial constraint concluded that vegetarian and vegan diets were safe during pregnancy if supplemented with iron and B12 [ 52 ]. However, vegans should plan their diets well, as they have an increased risk of not consuming enough protein when compared to omnivores and vegetarians [ 53 ]. An Australian study compared vegetarian and non-vegetarian women with GDM from South Asia in Australia found that the vegetarian GDM group received 14 ± 3% of their energy intake from protein as compared to 17 ± 4% in non-vegetarians, but remained within the range of the non-vegetarians supporting the feasibility of a vegetarian diet [ 54 ]. Another meta-analysis found that, overall, a vegetarian diet was not associated with birth weight, but that Asian women had a higher risk of delivering babies with low birth weight when compared to Caucasian women [ 55 ]. In poor rural areas of Asia, living a life as a vegetarian is more often a result of low income than a choice of lifestyle and lack of micronutrients e.g., vitamin B12 [ 56 ] may explain the association between vegetarianism and low birth weight.

A randomized clinical trial (RCT) of animal vs. soy protein applied for six weeks in 68 women with GDM showed lower fasting glucose, lower insulin levels, lower HOMA-IR, and lower triglyceride levels in the plant protein group. The women were randomized to receive protein from either 70% animal or 70% plant protein (half being from soy protein)—both arms were identical in the amount of protein received [ 57 ]. Another RCT on soy protein-based protein rich diet vs. high fibre complex diet in GDM showed a reduction in the need for exogenous insulin in the soy diet group. The arms of treatment were isocaloric. However, a low GI diet might explain the results rather than the protein itself [ 58 ].

4.4. High Protein Supplementation

Only one study on high protein supplementation during pregnancy has been performed. A RCT was performed in 1980 in poor African American women at risk of having infants with low birth weight. The high protein content of the supplementation (74.2 g/day) was associated with very early premature births, neonatal deaths, and growth retardation up to 37 weeks of gestational age [ 59 ]. It is unclear whether the adverse effects occurred because of the study population being unaccustomed to the high protein supplementation or if the results would have been different in populations of normal weight, well-nourished women, and women with GDM. However, the results of the study and lack of other studies of high protein intake during pregnancy implies that one should be reluctant regarding diets exceeding the recommend intake of protein during pregnancy—NGTP or diabetic pregnancies.

4.5. Pre-Meals and GDM

Pre-meals of protein administered prior to a meal have shown promising results on the postprandial blood glucose in non-pregnant healthy individuals and individuals with T2DM [ 60 , 61 ]. In a RCT of 52 women with GDM receiving either 8.5 g of casein hydrolysate ( n = 26) or placebo ( n = 26) prior to breakfast and dinner for eight days, the average blood glucose was decreased in the casein group [ 62 ]. Milk protein consists of 80% casein and 20% whey. Pre-meal whey protein has shown promising results with lower postprandial blood glucose in both healthy subjects, subjects with metabolic syndrome, and T2DM [ 60 , 61 , 63 , 64 ]. T2DM and GDM share similarities in their pathophysiology and, hence, women with GDM may display the same beneficial effect of whey pre-meals on blood glucose.

4.6. Summary, Protein

The current evidence suggests that increased protein intake from plants, lean meat and fish, and reduced intake of red and processed meat are beneficial in the treatment of GDM and may improve insulin sensitivity. The beneficial effect of plant protein on GDM might not be directly attributable to the source of protein, but rather to the reduction of other nutrients that are associated with an increased risk of GDM, such as carbohydrate [ 65 ] and saturated fat [ 66 ]. Furthermore, the results might not be generalizable to all ethnicities, as the majority of studies only investigated Asian and Middle Eastern women.

The recommended amount of fat in the dietary treatment of GDM is similar to the general nutrition advice for NGTP. The IOM recommends 20–35E% from fat [ 8 ], while the recommendation by NNR is the same as in non-pregnancy; 25–40E% [ 11 ]. A high intake of fat should be avoided, because this has been associated with infant adiposity, increased maternal inflammation and oxidative stress, and impaired muscle glucose uptake. Further, high fat diets might cause placental dysfunction [ 21 ].

5.1. Saturated Fatty Acids

The IOM recommends keeping the intake of trans fatty acids and saturated fatty acids as low as possible while consuming a nutritionally adequate diet during pregnancy [ 8 ]. NNR recommends, in general, that adults intake of saturated fat should not exceed 10E% [ 11 ]. To meet these recommendations, women with GDM can be instructed in choosing meat and meat products with a maximum of 10% fat, to choose low-fat dairy products, including choosing sour milk products with a maximum of 1.5% fat and limit intake of fatty dairy products, such as cream and butter.

5.2. Monounsaturated Fatty Acids

The recommendation for Cis-Monounsaturated fatty acids (MUFAs) by NNR is the same as in non-pregnancy; 10–20E%. In a study by Lauszus et al., 27 women with GDM were randomized to either high-carbohydrate diet or a high-MUFA diet. The 24 h diastolic blood pressure increased more in the carbohydrate group than in the MUFA-diet group. However, Lauszus et al. also found a significant difference in the intervention effect on insulin sensitivity in delta changes between groups, with a 15% increased insulin sensitivity in the high-carbohydrate diet and 34% decrease in the high-MUFA-diet [ 67 ]. More studies are needed if the recommendation for MUFA is to be changed in GDM as compared to a NGTP.

5.3. Polyunsaturated Fatty Acids

Long-chain polyunsaturated fatty acids (PUFAs) of the n -3 (α-linolenic acid) and n -6 series (linoleic acid) are the most important fatty acids for foetal growth and development [ 68 , 69 ]. n -3 and n -6 serve as essential components of cell membranes. Additionally, they are precursors for the synthesis of eicosanoids, which are important in the development of foetal nervous, immune, visual, and vascular systems [ 70 , 71 , 72 ]. The depletion of long-chain PUFAs in foetal tissues are associated with behavioural, cognitive, and visual abnormalities later in life in NGTP [ 68 ]. Furthermore, low levels of n -3 and n -6 during pregnancy have been shown to be correlated with preterm birth or foetal growth retardation in NGTP [ 73 ]. NNR recommends 5–10E% from PUFAs and a minimum of 1E% n -3 fatty acids in general for adults. A total intake of 2.7 g/day n -3 is considered to be safe during pregnancy [ 11 ].The IOM recommends 5–10E% n -6 and 0.6–1.2 E% n -3 with a minimum of 13 g/d of n -6 and a minimum of 1.4 g/day of n -3 during pregnancy [ 8 ]. An intake of a minimum of 350 g of fish per week, of which 200 g should be fatty fish, will ensure that the patients follow these recommendations. However, pregnant women should avoid predatory fish, due to the content of heavy metals, and salmon from the Baltic sea, due to pollution [ 74 ].

With regard to supplements with PUFAs, the evidence is not clear, as studies have shown conflicting results. These are plausibly reflecting the nature of long-chain PUFAs ingested, type of supplement, dose, and on the outcome evaluation. However, some studies with fish oil supplements have shown positive results in women with GDM. In an RCT by Jamilian et al., women with GDM were randomized to either 1000 mg omega-3 acids from flaxseed oil plus 400 IU vitamin E supplements or placebo for six weeks. A positive effect on biomarkers of oxidative stress and inflammation was found together with a significant rise in the total antioxidant capacity, nitric oxide, a significant decrease in plasma malondialdehyde, and a lower incidence of hyperbilirubinemia in new-borns. There was no effect on new-born outcomes (e.g., caesarean section, preterm delivery, or macrosomia >4000g) or C-reactive protein levels [ 75 ]. In another RCT by Jamilian et al., 40 women with GDM were randomly allocated to either 1000 mg fish oil capsules or placebo twice a day for six weeks. Fish oil capsules improved gene expression that was related to insulin, lipids, and inflammation; proliferator-activated receptor gamma was upregulated, and low-density lipoprotein receptor, Interleukin-1, and tumor necrosis factor alpha were downregulated. Fish oil supplement, as compared to placebo, also led to a significant reduction in FPG, serum triglycerides, and a significant increase in LDL- and HDL-cholesterol levels. Further, a significant reduction in high-sensitivity C-reactive protein, in those who received fish oil supplements, was found. However, Jamilian et al. did not find any effect on serum insulin, total cholesterol levels, or HOMA-IR [ 76 ]. In a study conducted by Samimi et al., a significant difference in changes in serum insulin and HOMA-IR was found in those women with GDM, who received fish oil supplements when compared to placebo. However, Samimi et al. did not find any effect on FPG [ 77 ]. Contrary to these results, a systematic review from 2016 did not find any effect of fish oil supplements on FPG, Homeostatic model assessment-Beta cell function, or lipid profiles. It was concluded that there is not enough evidence to support the routine use of fish oil supplements during pregnancy in the treatment of diabetes [ 78 ].

5.4. Summary, Fat

Women with GDM can be recommended an intake of 20–35E% from fat. The intake of saturated fat should be limited, and special focus should be placed on ensuring a sufficient intake of n -3 fatty acids. Despite some studies reporting a positive effect of fish oil supplementation, there are still conflicting results and, based on the current evidence, routine supplements of fish oil cannot be recommended or refuted, whereas women with GDM are recommended an intake of 350 g/week of fish as in NGTP.

6. Vitamins, Minerals and Tracers

During pregnancy, the need for vitamins and minerals increases [ 8 , 11 , 79 ]. There is not sufficient evidence to suggest that vitamin and mineral requirements for women with GDM should be different from normoglycaemic women or to suggest a specific optimal vitamins and minerals intake for women with GDM.

Well-nourished women may not need multiple-micronutrient supplements to satisfy daily requirements, but individual adjustments should be made upon the women’s specific needs. If pregnant women do not consume an adequate diet, then the IOM recommends multiple-micronutrient supplements [ 80 ]. As a minimum, there are recommendations for supplementation with folic acid, vitamin D, and iron. Any need for calcium supplement must be based on intake of dairy products. These micronutrients are discussed in more detail below and Table 5 shows recommendations.

Recommendation of specific micronutrients in pregnancy.

6.1. Vitamin B9/Folic Acid

Folates are important vitamins in pregnancy. Folate is critical for the synthesis of nucleic acids and, thus, cell division, therefore being important in the foetal growth. If the maternal folate level is low, then the risk of low birth weight and neural tube defects increases. Supplementation with folic acid (the synthetic structure of the folate family) during the periconceptional period has been shown to reduce the risk of these outcomes in NGTP [ 81 , 82 , 83 ]. The IOM recommends a daily intake of 600 µg/d during pregnancy [ 8 ], while the Nordic Council of Ministers 2014 has a lower recommendation of 500 µg/d in pregnancy [ 11 ]. A daily supplement of 400 µg folic acid/d may be recommended for all women of childbearing age and during the first 12 week of gestation to avoid low levels of folate in the mother at conception and ensure sufficient dietary intake.

Of notice, the form of folate substitution might be relevant to take into consideration. Common genetic variations in the genes encoding proteins that are involved in folate metabolism can lead to a lower conversion rate of folate to the active form, L-methylfolate. Recently, focus has been put on supplementation with L-methylfolate rather than folic acid. Apparently, women with such genetic mutations may benefit from direct supplementation with L-methylfolate [ 84 ].

Some studies have found that homocysteine levels, which are a marker of low folate or vitamin B12 status, are higher in women with GDM as compared to non-diabetic pregnant women. As an example, a cross-sectional study conducted by Guven et al. showed a higher homocysteine concentration in second trimester. However, folate and vitamin B12 levels did not differ between groups [ 85 ] and, at present, the same recommendations as for NGTP apply to women with GDM.

6.2. 25-Hydroxyvitamin D

The IOM recommends a dietary intake of 5.0 µg vitamin D/d during pregnancy [ 8 ], while NNR, which covers the Nordic countries, where serum 25(OH)D concentrations are often low in winter, recommends 10 µg/d during pregnancy [ 11 ]. These recommendations for NGTP are also currently applicable to women with GDM.

Increasing evidence suggests that vitamin D may play an important role in modifying the risk of diabetes [ 86 ], as vitamin D acts directly on the pancreatic beta cell by increasing insulin secretion, and indirectly by attenuating systemic inflammation that is associated with insulin resistance [ 87 , 88 ]. Many cross-sectional and prospective observational studies have shown an inverse association between vitamin D status and the prevalence or incidence of type 2 diabetes [ 86 ]. Therefore, vitamin D is also the micronutrient that has been studied most extensively in relation to GDM. Several studies indicate a significant inverse relation of serum 25OHD and the incidence of GDM, but it is not clear whether this association is causal [ 89 ] and large RCTs of the effects of vitamin D in women with GDM are sparse. However, in a RCT by Asemi et al., 54 women with GDM received either placebo capsules or vitamin D capsules (50.000 IU) twice during the six week study period and intake of vitamin D supplements led to a significant decrease in FPG and insulin resistance assessed by HOMA-IR [ 90 ]. In another RCT, women with GDM were randomized to either placebo or 200 IU, 2000 IU, or 4000 IU vitamin D daily. Insulin levels, HOMA-IR, and total cholesterol were significantly reduced in the group receiving 4000 IU of vitamin D [ 91 ]. In a recent meta-analysis, including six RCTs, it was found that vitamin D supplementations improved insulin resistance and LDL cholesterol, but had no beneficial effect on FPG, insulin, HbA1c, total-, HDL-cholesterol, and triglycerides concentrations [ 92 ].

The effects of vitamin D supplementation in GDM are equivocal and the available trials have been conducted in different settings with differences in subject populations, length of intervention, and forms of vitamin D supplementation. Confounding variables, such as ethnicity and seasonality, add to the complexity of vitamin D studies and vitamin D can be seen as a proxy for a healthy lifestyle with an active life outside being exposed to the sun. At present, it is therefore difficult to conclude whether vitamin D can reduce the risk of developing GDM and/or improve glycaemic control in women with GDM and vitamin D deficiency/insufficiency, as there is a need for larger well-designed RCTs that evaluate interventions together with the evaluation of confounding factors.

6.3. Calcium

The requirement of calcium is increased during pregnancy [ 93 ]. However, the Nordic Council of Ministers 2014 did not find enough data to draw firm conclusions on potential association between calcium intake during pregnancy and bone health in the offspring. The recommended daily intake of 900 mg/day was kept unchanged from the 2004 to the 2012 updated version [ 11 ]. However, the IOM has a slightly higher recommendation during pregnancy of 1000 mg/day in women >19 years [ 8 ].

Whether supplementation is necessary depends on the woman’s food intake. However, calcium supplementation might have a potential positive effect on glycaemic control in women with GDM. Asemi et al. demonstrated a significant reduction in FPG in women with GDM who received 1000 mg calcium/d plus 50.000 U vitamin D3 supplements twice during a six week intervention when compared to placebo. In the same study, Asemi et al. also found a significant reduction in the serum insulin levels and HOMA-IR. It was concluded that calcium plus vitamin D supplementation in women with GDM had beneficial effects on their metabolic profile [ 93 ].

In conclusion, it can be advocated to ensure a minimum intake of 900–1000 mg calcium per day during pregnancy in women with GDM. Therefore, it can be recommended that all pregnant women receive e.g., 0.5 L of milk product per day, less when supplemented with cheese, or that 900–1000 mg calcium is ingested daily from other sources of calcium. If the woman is unable to meet these recommendations, then there may be a need of a daily supplement of 500 mg of calcium throughout pregnancy.

Iron deficiency is the most common micronutrient deficiency in pregnancy and during childbearing years. Women have increased needs for iron due to the iron losses during menstrual bleeding [ 11 ]. Additionally, many women have small iron stores, when they become pregnant and are not gaining appropriate amounts of iron in their diet to cover the increased need during pregnancy. Because of this, some countries recommend iron supplements of 40 mg as early as week 10 of pregnancy [ 94 ]. Maternal iron need increases during pregnancy in order to accommodate the growth and maintenance of the foetus and uterus and the increased red blood cell count. Further, there is an expected iron loss when giving birth [ 11 ]. The IOM recommends a daily intake of 27 mg/d during pregnancy [ 8 ], while iron supplementation of 40 mg/d from week 18–20 of gestation has been suggested by the Nordic Council of Ministers 2014, in order to reduce the risk of low birth weight and preterm delivery [ 11 , 95 ].

However, whether iron supplementation during pregnancy is necessary or a toxic supplement is a controversial topic. The literature suggests that iron influences glucose metabolism [ 95 ]. In a cohort study conducted by Bo et al., an association between the intake of iron supplements and a higher oral glucose tolerance test glucose values in women with GDM was found [ 95 ]. Today, there is not enough evidence to suggest a different recommendation for iron intake in women with GDM than what applies to NGTP.

7. Probiotics

In recent years, the role of gut microbiota in regulating metabolism has become a hot topic of investigation. Thus, gut microbiota may play a significant role in the development of obesity and may also have an important impact on glucose homeostasis [ 96 ]. Moreover, the results indicate that, in pregnancy, the changes in gut microbiota from the first to the third trimester may contribute to the maternal metabolic changes [ 97 ]. In a Danish study, the gut microbiota profiles were investigated in 50 women with GDM and in 157 pregnant women with normal glucose tolerance and it was reported that, in the third trimester of pregnancy, GDM was associated with an altered gut microbiota as compared to that of NGTP [ 98 ]. Accordingly, several studies have been performed to determine whether probiotics could be beneficial for the prevention or treatment of GDM. However, the results of the many available studies are equivocal. In a Finnish RCT study, 439 pregnant women with overweight or obesity were divided into four intervention groups with fish oil + placebo, probiotics ( Lactobacillus rhamnosus and Bifidobacterium animalis ssp lactis ) + placebo, fish oil + probiotics, and placebo + placebo. The primary outcomes were incidence of GDM and change in fasting glucose in the intervention period, but no benefits in lowering the risk of GDM or improving glucose metabolism was found in any of the groups [ 99 ]. Callaway et al. performed a large double-blind RCT, including 411 women, in order to determine whether probiotics ( Lactobacillus rhamnosus and Bifidobacterium animalis ssp lactis ) that were administered from the second trimester in women with overweight or obesity could prevent GDM. Unfortunately, GDM could not be prevented by the intervention [ 100 ]. In an Irish RCT, 149 women with GDM received either a probiotic capsule ( Lactobacillus salivarius ) or placebo once daily from diagnosis of GDM to delivery and no effect on glycaemic control was found [ 101 ].

However, two meta-analyses have shown that the use of probiotics was associated with an improved glucose and lipid metabolism in pregnant women, and could tentatively reduce the risk of gestational diabetes [ 102 , 103 ]. Another meta-analysis showed that supplementation with probiotic reduced insulin resistance (HOMA-IR) and fasting serum insulin in women with gestational diabetes significantly, as compared to pregnant women with normal glucose tolerance [ 104 ]. In a recent study conducted by Kijmanawat et al., women with GDM were randomized to probiotics ( Lactobacillus and Bifidobacterium ) or placebo for four consecutive weeks and a significant improvement in glucose metabolism in the probiotic group, regarding fasting glucose, insulin, and HOMA-IR was found [ 105 ]. Additionally, in a study conducted by Karamali et al., where 60 women with GDM were included to determine the effects of probiotic supplementation on glycaemic control and lipid profiles after six weeks and beneficial effects on glycaemic control, triglycerides, and VLDL cholesterol were reported. The study was a double blind RCT where the women either received a probiotic capsule (containing three viable freeze-dried strains: Lactobacillus acidophilus , L. casei , and Bifidobacterium bifidum ) or a matching placebo [ 106 ].

Summary, Probiotics

The question of whether gut microbiota modification could be an effective tool in improving glycemic control and reducing insulin resistance in pregnant women with GDM is complicated. The results differ as the human gut houses a complex microbial ecosystem and the present studies have used different pre-or probiotics or multi-strain probiotics, making it difficult to compare studies and to make a final conclusion at this point.

8. Nutrition Counselling

In a recent meta-analysis, including 18 RCTs involving 1151 women with GDM, a moderate effect of dietary interventions on maternal glycaemic outcomes, including changes in fasting, post-breakfast, and postprandial glucose levels, and the need for medication treatment was found [ 6 ]. For neonatal outcomes, including 16 RCTs and 841 women with GDM, it was found that modified dietary interventions were associated with lower infant birth weight and less macrosomia [ 6 ]. These associations were found despite a high heterogeneity between studies [ 6 ], which indicated that several methods can be used and the dietary guidance should probably be adapted to the individual patient.

The American Diabetes Association recommends that women with GDM receive an individualized nutrition plan as a part of medical nutrition therapy. The nutrition plan should be developed in collaboration between the women and an experienced dietician [ 10 ]. The adjustment of the nutrition plan should be continuous and based upon self-glucose monitoring, appetite, and weight-gain patterns, as well as consideration for maternal dietary preferences and work, leisure, and exercise. If insulin therapy is added to nutrition therapy, a primary goal is to maintain carbohydrate consistency at meals and snacks in order to facilitate insulin adjustment.

9. Physical Activity

In non-pregnant individuals, it is well established that physical activity reduces insulin resistance by stimulating the glucose transporters on the surface of skeletal muscle cells and thereby improving glucose uptake [ 107 , 108 , 109 ]. Interestingly, whereas many studies have addressed the impact of physical activity on various outcomes in pregnancy in general, only a paucity of studies have addressed the impact of physical activity on maternal blood glucose levels and glycaemic control during pregnancy in women with GDM.

9.1. Short Term Effects of Physical Activity in Pregnancy on Maternal Blood Glucose Levels

Acute bouts of physical activity appear to influence maternal glucose levels on short term. Treadmill exercise for 30 min reduces blood glucose and insulin levels in healthy pregnant women [ 110 ]. Among women at risk of GDM 20 min of moderate intensity cycling after an oral glucose tolerance test reduced blood glucose excursions and insulin levels within one to two hours after glucose ingestion [ 111 ]. However, a long-term effect was not observed, when evaluating continuous glucose measurements for up to 48 h after physical activity [ 111 ]. Similar findings were observed after walking, i.e., women at risk of GDM had decreases in blood glucose levels that were associated with the duration and intensity of the exercise with glucose levels aligning within a few hours after physical activity [ 112 ].

Similar observations have been made among women with GDM. Light intensity walking after a meal reduced 1-h blood glucose levels, but not 2-h values [ 113 ]. Moderate intensity walking after a meal had slightly longer lasting effects on blood glucose levels with effects visible for two to three hours where after blood glucose levels again aligned [ 114 ]. Cycling at mild and moderate intensity yielded similar results as after walking, i.e., a short-lasting decreasing effect on blood glucose levels when compared to the resting condition in a “dose-dependent” matter, i.e., larger effects with more intensified physical activity [ 115 ].

In the above-mentioned studies, blood glucose levels after physical activity were comparable after minutes to hours. Thus, is appears reasonable that acute bouts of physical activity have short lasting effects on maternal glucose levels. A continuous program of physical activity appears to be necessary for longer-term effects to be seen.

9.2. Longer-Term Effects of Physical Activity

Longer-term effects of bouts of physical activity are more diverse, as the effects could be the direct influence upon glucose metabolism or it could be effects relating to pregnancy outcomes for which glucose metabolism plays a role, i.e., birth weight and a range of pregnancy complications, such as hypertensive disorders, macrosomia, shoulder dystocia, and neonatal hypoglycaemia and jaundice.

Resistance exercise has been reported to be effective in reducing the need for insulin in GDM pregnancy [ 116 ], and moderate intensity cycling three times weekly in combination with diet was able to yield weekly blood glucose levels that were comparable to insulin combined with diet [ 117 ]. Again, exercising women managed to stay without any need for insulin [ 117 ]. In contrast, combined cycling exercise at moderate intensity alternated by walking three to four times weekly did not induce changes in daily blood glucose measurements or in HbA1c values [ 118 ].

The effects of physical exercise during GDM pregnancy on pregnancy outcomes have not been thoroughly examined. Often, study protocols have combined physical activity with other lifestyle modifications, so that the individual contributions from diet, physical activity, coaching, or other included interventions on the study outcomes may be difficult to discern. In a 2018 Cochrane overview of reviews, it was concluded that, in general, only limited effects of exercise as the sole intervention in GDM pregnancy could be documented. Of the palette of interventions that could be explored, the best documentation was available for the combination of healthy eating, physical exercise, and self-monitoring of blood glucose levels. In combination, these efforts could reduce the risk of LGA-babies, but probably at the cost of more prevalent inductions of labour [ 119 ]. Thus, the beneficial effects of lifestyle interventions in pregnancy could be accompanied by an introduction of side effects or potential harms in pregnancy [ 119 ].

9.3. Recommendations for Exercise in GDM Pregnancy

In Denmark, pregnant women are recommended at least 30 min of (unspecified) moderate intensity physical exercise daily. There are no specific recommendations for physical activity or exercise that addresses women with GDM, but women with GDM are encouraged to exercise more than the recommendations in NGTP [ 120 ]. Similar recommendations are found in the Canadian guidelines for physical activity throughout pregnancy [ 121 ], in which 150 min of moderate intensity physical activity each week on at least three separate days is recommended for women independent of GDM status.

Exercise three times a week for 40 to 60 min at 65 to 75% of the age-corrected heart rate maximum has been suggested for women with GDM [ 122 ]. Activities could be circuit training, walking, or cycling, but the need for studies testing the most optimal physical activity was acknowledged [ 122 ].

Thus, physical activity during pregnancies complicated with GDM is recommended, and moderate intensity activity appears to be the choice agreed upon. However, currently, there is no common agreement on the type, frequency, and duration of physical activity that would be beneficial or even most optimal. Further, the optimal gestational age or the optimal range of gestational weeks for intervention needs to be clarified.

9.4. Societal Interventions

The increased prevalence of diabetes mellitus in especially industrialized countries have led to considerations regarding possible societal interventions. The construction of urban environments aimed at facilitating physical activity has been considered. Easy access to minor and local sport facilities might be an opportunity to improve physical activity for some individuals; however, this strategy is dependent on whether the individuals will use such facilities. Urban planning may be a means to increase the level of physical activity on a population level, and it has been reported that increasing the “walkability” of a neighbourhood is associated with a lower incidence of diabetes [ 123 ]. Walking has been suggested to be an especially attractive means of physical activity during pregnancy [ 124 ]. In GDM, a single study recently reported on the relationship between neighbourhood walkability and variables that were related to GDM [ 125 ]. High neighbourhood walkability was, in general, associated to a lower pre-pregnant BMI and higher pre-pregnant levels of physical activity. In pregnancy, though, increasing walkability of neighbour surroundings was not associated to GWG, insulin sensitivity, glycaemia, or beta cell function [ 125 ] Additionally, no difference in GDM prevalence was observed across the different classes of walkable surroundings [ 125 ].

Despite low evidence for the time being of the effect of walking on the risk for GDM in pregnancy, walking that is facilitated on both the individual and societal levels may prove to be a simple and obtainable way to introduce more physical energy expenditure in pregnancy [ 124 , 125 ].

9.5. Hindrances to Exercise in Pregnancy

During pregnancy, certain conditions may limit physical activity. Pre-existing medical conditions may limit the amount of physical activity that can be performed. Musculoskeletal or cardiac diseases may decrease the daily level of physical activity and preclude any invigorated physical activity. Additionally, conditions that are related to pregnancy may lead to the recommendation of immobilization or even bed rest, e.g., short cervix conditions or imminent premature delivery. Despite the lack of evidence for promoting immobilization of women with such complications, clinical practice implies that some degree of immobilization is often instituted. In the case of threatening preterm delivery, the administration of corticoid therapy for foetal lung maturation may further exacerbate insulin resistance, at least for days [ 126 ]. Furthermore, common conditions, like pelvic joint laxity and pelvic girdle discomfort, will often lead to cautious movements and decreased levels of physical activity. More uncommon, lower extremity varicose veins or even deep venous thrombosis may cause immobilization. Such conditions are primarily related to the third trimester of pregnancy, i.e., at the time of maximal insulin resistance.

9.6. Summary, Physical Activity

When GDM is present, single physical activities clearly has short term effects on blood glucose levels. However, sustainable effects are more complex to obtain. Long-lasting effects, be it on maternal blood glucose levels or on pregnancy outcomes in general, do with all likelihood depend on daily physical activity and may be further corroborated by a concomitant reduction in GWG. Measures to increase the daily level of physical activity and the strategy for exercise and physical activity in pregnancy with GDM still need further exploration.

10. Conclusions

A summary of the above recommendations is found in Table 6 . All women with GDM should be offered dietary advice by a clinical dietitian, as dietary counselling the cornerstone in the treatment of GDM. Knowledge of the impact of diet on blood glucose is of great importance in preventing complications, such as birth complications, caesarean section, LGA-babies, and type 2 diabetes, later in life. The woman should receive guidance on how to construct a varied diet and how to avoid hyperglycaemia. Particular efforts should focus on carbohydrate intake as both type, amount and distribution of carbohydrate are of major importance for the postprandial blood glucose. In general, the same recommendations for minerals and vitamins apply to women with GDM as in NGTP. In addition, physical activity of moderate intensity for at least 30 min daily or 150 min weekly should be encouraged, as this may contribute to improved glycaemic control.

Summary of recommendations.

d, daily; E%, energy precent; GDM, gestational diabetes mellitus; MUFAs, monounsaturated fatty acids; PUFAs, polyunsaturated fatty acids.

Abbreviations

Author Contributions

L.R., C.W.P., U.K. and J.F.—conceptualization, L.R., C.W.P., U.K., S.B.S., P.G.O. and J.F.—writing—review and editing. All authors have read and approved the final manuscript.

This research received no external funding. U.K. is supported by the Danish Diabetes Academy, funded by the Novo Nordisk Foundation. P.O. has received financial support from the Novo Nordisk Foundation. L.R., C.W.P., S.B.S. and J.F. have no financial disclosures. The funding sources have no role in the writing of this review.

Conflicts of Interest

The authors declare no conflict of interest regarding the publication of this paper.

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Gut microbiota dysbiosis, oxidative stress, inflammation, and epigenetic alterations in metabolic diseases.

1. Introduction

2. gut microbiota structure and gut microbiome development in mammals, 3. the gut microbiome and diet interaction, 4. gut dysbiosis and ros production, 5. metabolic impacts of gut dysbiosis involving epigenetic mechanisms, 6. gut microbiome, inflammation, ros, and dna methylome interactions, 7. transfer of gut microbiota-related metabolic diseases to the next generation through epigenetic mechanism, 8. dietary and probiotic interventions to modulate gut microbiome, ros, and metabolic diseases, 9. dietary and microbiome-induced health benefits mediated by epigenetic modifications, 10. conclusions, author contributions, acknowledgments, conflicts of interest.

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Mostafavi Abdolmaleky, H.; Zhou, J.-R. Gut Microbiota Dysbiosis, Oxidative Stress, Inflammation, and Epigenetic Alterations in Metabolic Diseases. Antioxidants 2024 , 13 , 985. https://doi.org/10.3390/antiox13080985

Mostafavi Abdolmaleky H, Zhou J-R. Gut Microbiota Dysbiosis, Oxidative Stress, Inflammation, and Epigenetic Alterations in Metabolic Diseases. Antioxidants . 2024; 13(8):985. https://doi.org/10.3390/antiox13080985

Mostafavi Abdolmaleky, Hamid, and Jin-Rong Zhou. 2024. "Gut Microbiota Dysbiosis, Oxidative Stress, Inflammation, and Epigenetic Alterations in Metabolic Diseases" Antioxidants 13, no. 8: 985. https://doi.org/10.3390/antiox13080985

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