literature review on gestational diabetes pdf

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Gestational diabetes mellitus—recent literature review.

1. Introduction

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

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

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

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

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Research design and methods, conclusions, article information, gestational diabetes mellitus and diet: a systematic review and meta-analysis of randomized controlled trials examining the impact of modified dietary interventions on maternal glucose control and neonatal birth weight.

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Jennifer M. Yamamoto , Joanne E. Kellett , Montserrat Balsells , Apolonia García-Patterson , Eran Hadar , Ivan Solà , Ignasi Gich , Eline M. van der Beek , Eurídice Castañeda-Gutiérrez , Seppo Heinonen , Moshe Hod , Kirsi Laitinen , Sjurdur F. Olsen , Lucilla Poston , Ricardo Rueda , Petra Rust , Lilou van Lieshout , Bettina Schelkle , Helen R. Murphy , Rosa Corcoy; Gestational Diabetes Mellitus and Diet: A Systematic Review and Meta-analysis of Randomized Controlled Trials Examining the Impact of Modified Dietary Interventions on Maternal Glucose Control and Neonatal Birth Weight. Diabetes Care 1 July 2018; 41 (7): 1346–1361. https://doi.org/10.2337/dc18-0102

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Medical nutrition therapy is a mainstay of gestational diabetes mellitus (GDM) treatment. However, data are limited regarding the optimal diet for achieving euglycemia and improved perinatal outcomes. This study aims to investigate whether modified dietary interventions are associated with improved glycemia and/or improved birth weight outcomes in women with GDM when compared with control dietary interventions.

Data from published randomized controlled trials that reported on dietary components, maternal glycemia, and birth weight were gathered from 12 databases. Data were extracted in duplicate using prespecified forms.

From 2,269 records screened, 18 randomized controlled trials involving 1,151 women were included. Pooled analysis demonstrated that for modified dietary interventions when compared with control subjects, there was a larger decrease in fasting and postprandial glucose (−4.07 mg/dL [95% CI −7.58, −0.57]; P = 0.02 and −7.78 mg/dL [95% CI −12.27, −3.29]; P = 0.0007, respectively) and a lower need for medication treatment (relative risk 0.65 [95% CI 0.47, 0.88]; P = 0.006). For neonatal outcomes, analysis of 16 randomized controlled trials including 841 participants showed that modified dietary interventions were associated with lower infant birth weight (−170.62 g [95% CI −333.64, −7.60]; P = 0.04) and less macrosomia (relative risk 0.49 [95% CI 0.27, 0.88]; P = 0.02). The quality of evidence for these outcomes was low to very low. Baseline differences between groups in postprandial glucose may have influenced glucose-related outcomes. As well, relatively small numbers of study participants limit between-diet comparison.

Modified dietary interventions favorably influenced outcomes related to maternal glycemia and birth weight. This indicates that there is room for improvement in usual dietary advice for women with GDM.

Gestational diabetes mellitus (GDM) is one of the most common medical complications in pregnancy and affects an estimated 14% of pregnancies, or one in every seven births globally ( 1 ). Women with GDM and their offspring are at increased risk of both short- and longer-term complications, including, for mothers, later development of type 2 diabetes, and for offspring, increased lifelong risks of developing obesity, type 2 diabetes, and metabolic syndrome ( 2 – 6 ). The adverse intrauterine environment causes epigenetic changes in the fetus that may contribute to metabolic disorders, the so-called vicious cycle of diabetes ( 7 ).

The mainstay of GDM treatment is dietary and lifestyle advice, which includes medical nutrition therapy, weight management, and physical activity ( 8 ). Women monitor their fasting and postmeal glucose levels and adjust their individual diet and lifestyle to meet their glycemic targets. This pragmatic approach achieves the glycemic targets in approximately two-thirds of women with GDM ( 8 ). However, despite the importance of medical nutrition therapy and its widespread recommendation in clinical practice, there are limited data regarding the optimal diet for achieving maternal euglycemia ( 8 – 11 ). It is also unknown whether the dietary interventions for achieving maternal glycemia are also effective for reducing excessive fetal growth and adiposity ( 12 ).

Different dietary strategies have been reported including low glycemic index (GI), energy restriction, increase or decrease in carbohydrates, and modifications of fat or protein quality or quantity ( 12 – 14 ). Three recent systematic reviews have been performed examining specific diets and pregnancy outcomes ( 15 – 17 ). Viana et al. ( 16 ) and Wei et al. ( 15 ) concluded that low-GI diets were associated with a decreased risk of infant macrosomia. However, the most recent systematic review from Cochrane, including 19 trials randomizing 1,398 women, found no clear difference in large for gestational age or other primary neonatal outcomes with the low-GI diet ( 17 ). The primary maternal outcomes were hypertension (gestational and/or preeclampsia), delivery by cesarean section, and type 2 diabetes, outcomes for which most trials lacked statistical power, even when dietary subgroups were combined. Remarkably, no systematic reviews examined the impact of modified dietary interventions on the detailed maternal glycemic parameters, including change in glucose-related variables, the outcomes that are most directly influenced by diet.

To address this knowledge gap, we performed a systematic review and meta-analysis of randomized controlled trials to investigate whether modified dietary interventions (defined as a dietary intervention different from the usual one used in the control group) in women with GDM offer improved glycemic control and/or improved neonatal outcomes when compared with standard diets.

In accordance with a published protocol (PROSPERO CRD42016042391), we performed a systematic review and meta-analysis. Reporting is in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( 18 ). An international panel of experts was formed by the International Life Sciences Institute Europe. This panel determined the review protocol and carried out all aspects of the review.

Data Sources and Search Strategy

The following databases were searched for all available dates using the search terms detailed in Supplementary Table 1 : PubMed, MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science Core Collection, Applied Social Sciences Index and Abstracts, ProQuest, ProQuest Dissertations & Theses—A&I and UK & Ireland, National Institute for Health and Care Excellence evidence search, Scopus, UK Clinical Trials Gateway, ISRCTN, and ClinicalTrials.gov . The initial search was performed in July 2016. An updated search of MEDLINE, Embase, CENTRAL, and CINAHL was performed on 3 October 2017 using the same search terms.

A hand-search of relevant reviews and all included articles was conducted to identify studies for potential inclusion. As well, experts on the panel were consulted for the inclusion of additional articles. Reference management was carried out using EndNote.

Study Selection

All titles and abstracts were assessed independently and in duplicate to identify articles requiring full-text review. Published studies fulfilling the following criteria were included: randomized controlled trials, evaluated modified dietary interventions on women with GDM, glucose intolerance or hyperglycemia during pregnancy, reported-on primary maternal and neonatal outcomes, included women aged 18–45 years, had a duration of 2 weeks or more, and were published in English, French, Spanish, Portuguese, Italian, Dutch, German, or Chinese. We excluded studies that included participants with type 1 or type 2 diabetes if data for participants with GDM were not presented independently, if dietary characteristics were not available, if the study was in animals, or if the study did not report outcomes of interest. We did not include studies of nutritional supplements such as vitamin D or probiotics as recent reviews have addressed these topics ( 19 , 20 ).

All citations identified after title and abstract assessment were full-text reviewed in duplicate. Reasons for exclusion at the full-text review stage were recorded. Any disagreements between reviewers were resolved by consensus and with consultation with the expert group when required.

Data Extraction

Data from included studies were extracted in duplicate using prespecified data extraction forms. Extracted data elements included study and participant demographics, study design, diagnostic criteria for GDM, glucose intolerance or hyperglycemia, funding source, description of modified dietary intervention and comparator, and maternal and neonatal outcomes. For studies with missing data, inconsistencies, or other queries, authors were contacted. Record management was carried out using Microsoft Excel and RevMan.

For articles providing information on maternal weight, fasting glucose, postprandial glucose, HbA 1c , or HOMA insulin resistance index (HOMA-IR) at baseline and postintervention but not their change, change was calculated as the difference between postintervention and baseline. Standard deviations were imputed using the correlation coefficient observed in articles reporting full information on the variable at baseline and postintervention and its change or a correlation coefficient of 0.5 when this information was not available ( 21 ). As studies differed in postprandial glucose at baseline, glycemic control at study entry was not considered to be equivalent in both arms, and thus continuous glucose-related variables at follow-up are reported as change from baseline.

Data Synthesis

The primary outcomes were maternal glycemic outcomes (mean glucose, fasting glucose, postprandial glucose [after breakfast, lunch, and dinner and combined], hemoglobin A 1c [HbA 1c ], assessment of insulin sensitivity by HOMA-IR, and change in these parameters from baseline to assessment; medication treatment [defined as oral diabetes medications or insulin]) and neonatal birth weight outcomes (birth weight, macrosomia, and large for gestational age).

Data were pooled into relative risks (RRs) or mean differences with 95% CI for dichotomous outcomes and continuous outcomes, respectively. Meta-analysis was performed using random-effects models. A prespecified analysis stratified by type of diet and quality assessment was performed to explore potential reasons for interstudy variation. Heterogeneity was assessed using I 2 statistics. Small study effects were examined for using funnel plots. Analyses were conducted using RevMan version 5.3. Pooled estimation of birth weight in the study and control arms, both overall and according to the specific diet intervention, was performed using Stata 14.0.

Quality Assessment

Methodological quality and bias assessment was completed by two reviewers. Risk of bias was assessed using the Cochrane Collaboration tool, which rates seven items as being high, low, or unclear for risk of bias ( 21 ). These items included random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective outcome reporting, and other potential sources of bias ( 21 ). A sensitivity analysis was performed excluding articles with relevant weaknesses in trial design or execution.

The overall quality of the evidence was also assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE) working group guidelines ( 21 ). GRADE was assessed for all primary and secondary outcomes, both maternal and neonatal, but without subgroup analysis per different dietary intervention for each outcome measure.

We screened 2,269 records for potential inclusion, and 126 articles were reviewed in full ( Supplementary Fig. 1 ). Eighteen studies ( 12 – 14 , 22 – 36 ) were included in the meta-analysis with a total of 1,151 pregnant women with GDM.

Study Characteristics

The types of modified dietary intervention included low-GI ( n = 4), Dietary Approaches to Stop Hypertension (DASH) ( n = 3), low-carbohydrate ( n = 3), fat-modification ( n = 2), soy protein–enrichment ( n = 2), energy-restriction ( n = 1), high-fiber ( n = 1), and ethnic diets (i.e., foods commonly consumed according to participant’s ethnicity) ( n = 1) and behavioral intervention ( n = 1). Details of the study characteristics are included in Table 1 . Most trials were single centered and had small sample sizes (range 12–150). Only two trials (one each from Spain and Australia) included over 100 participants, nine had 50–100 participants, and seven studies had fewer than 50 participants. They were performed in North America, Europe, or Australasia and all had a duration of at least 2 weeks. The ethnicity of participants was reported in seven studies ( 12 , 13 , 26 , 29 , 31 , 32 , 34 ).

Characteristics of studies included

Unless otherwise stated, the units are kcal/day for energy, % for carbohydrate, protein, and fat. OGTT, oral glucose tolerance test.

*Reported actual dietary intake. When not reported, prescribed dietary intake is reported.

†Intervention is defined as dietary intervention different from the usual dietary intervention used in the control group.

‡Indicates prescribed diet.

§The control and intervention groups were reversed for the purpose of meta-analysis so it could be included in the low-carbohydrate group.

Most studies assessed individual dietary adherence using food diaries ( 13 , 23 – 36 ). Although most studies did report an overall difference in dietary composition between the intervention diet and control diet, few studies reported a detailed assessment of dietary adherence. Only five studies used a formal measure of adherence ( 24 , 25 , 29 , 33 , 34 ), and four of them reported data ( 25 , 29 , 33 , 34 ). Adherence ranged from 20% to 76% in the control groups and 60% to 80% in the intervention groups.

Participant Characteristics

When baseline characteristic data were pooled, women in the intervention group were older than women in the control group (pooled mean difference 0.60 years [95% CI 0.06, 1.14]) and had higher postprandial glucose (5.47 [0.86, 10.08]), most influenced by the DASH and ethnic diet studies. There was no overall significant difference between the intervention and control groups for BMI, gestational age at enrollment, fasting glucose, HbA 1c , or HOMA-IR.

Maternal Glycemic Outcomes for All Modified Dietary Interventions

Pooled risk ratios in 15 studies involving 1,023 women demonstrated a lower need for medication (RR 0.65 [95% CI 0.47, 0.88]; I 2 = 55) ( Table 2 ). Thirteen studies ( n = 662 women) reported fasting glucose levels, nine ( n = 475) reported combined postprandial glucose measures, and three ( n = 175) reported post-breakfast glucose measures. Pooled analysis demonstrated a larger decrease in fasting, combined postprandial, and post-breakfast glucose levels in modified dietary interventions (mean −4.07 mg/dL [95% CI −7.58, −0.57], I 2 = 86, P = 0.02; −7.78 mg/dL [−12.27, −3.29], I 2 = 63, P = 0.0007; and −4.76 mg/dL [−9.13, −0.38], I 2 = 34, P = 0.03, respectively) compared with control group. There were no significant differences in change in HbA 1c (seven studies), HOMA-IR (four studies), or in post-lunch or -dinner glucose levels (two studies).

Pooled analyses of primary maternal glycemic and infant birth weight outcomes

Neonatal Birth Weight Outcomes for All Diets

Pooled mean birth weight was 3,266.65 g (95% CI 3,172.15, 3,361.16) in the modified dietary intervention versus 3,449.88 g (3,304.34, 3,595.42) in the control group. Pooled analysis of all 16 modified dietary interventions including 841 participants demonstrated lower birth weight (mean −170.62 g [95% CI −333.64, −7.60], I 2 = 88; P = 0.04) and less macrosomia (RR 0.49 [95% CI 0.27, 0.88], I 2 = 11; P = 0.02) compared with conventional dietary advice ( Table 2 and Fig. 1 ). There was no significant difference in the risk of large-for-gestational-age newborns in modified dietary interventions as compared with control diets (RR 0.96 [95% CI 0.63, 1.46], I 2 = 0; P = 0.85).

Forest plot of birth weight for modified dietary interventions compared with control diets in women with GDM. Reference citations for studies can be found in Table 1 . CHO, carbohydrate; IV, inverse variance.

Subgroup Meta-analysis by Types of Dietary Interventions

Pooled analysis of low-GI diets showed a larger decrease in fasting ( 26 , 29 , 30 ), postprandial, and post-breakfast glucose compared with control diets ( 26 , 30 ) ( Table 2 ). However, the pooled analysis of the DASH diet showed significant favorable modifications in several outcomes, including change in fasting ( 22 , 36 ) and postprandial glucose ( 22 ), HOMA-IR ( 35 ), HbA 1c ( 22 ), medication need ( 22 , 23 , 36 ), infant birth weight ( 23 , 36 ), and macrosomia ( 23 , 36 ) ( Tables 2 and 3 ). Last, pooled analysis of the soy protein–enriched diet demonstrated a significant decrease in medication use and birth weight ( 14 , 27 ) ( Tables 2 and 3 ). One soy–protein intervention ( n = 68 participants) described significantly lower HOMA-IR ( 27 ) ( Table 2 ).

Sensitivity analysis of primary maternal glycemic and infant birth weight outcomes

Behavioral (one study) and ethnic-specific modified dietary interventions (one study) were included. The behavioral change dietary intervention reported significant differences in change in postprandial glucose and in HbA 1c ( Table 2 ) ( 24 ). The ethnic diet study demonstrated a significantly larger decrease in fasting and postprandial glucose ( Table 2 ) ( 34 ). Fat-modification, low-carbohydrate, and energy-restriction diets were not associated with a significant difference in our primary outcomes in the stratified analysis.

Secondary Outcomes

Weight gain from inclusion was lower for low-carbohydrate diets and cesarean birth for DASH diets ( Supplementary Table 2 ). Specific diet interventions did not show significant between-group differences in maternal gestational weight gain throughout pregnancy, preeclampsia/eclampsia, neonatal hypoglycemia as defined by the authors, preterm birth, neonatal intensive care unit admission, or small-for-gestational-age newborns ( Supplementary Tables 2 and 3 ).

Sensitivity Analysis of Primary Outcomes

Sensitivity analysis was performed to explore reasons for heterogeneity and to assess outcomes when studies with methodological concerns were removed. We were unable to include four studies ( 22 , 23 , 34 , 36 ), including all the DASH diet studies, where clarification of certain aspects of the results could not be obtained, even after a direct approach to the authors. The authors of the ethnic diet study responded to queries but did not provide the required information regarding gestational age at randomization ( 34 ). After these studies are removed, the changes in postprandial glucose (mean −5.90 mg/dL [95% CI −7.93, −3.88], I 2 = 0; P = 0.0001), post-breakfast glucose levels (−4.76 mg/dL [−9.13, −0.38], I 2 = 34; P = 0.03), and birth weight (−74.88 g [−144.86, −4.90], I 2 = 1; P = 0.04) remained significant when all diets were combined ( Table 3 ). Furthermore, the heterogeneity in most primary outcomes decreased after removal of these four studies.

When dietary subgroups were assessed, low-GI diets had significant differences in changes in fasting (mean −5.33 mg/dL [95% CI −6.91, −3.76]) ( 26 , 29 , 30 ), postprandial (−7.08 mg/dL [−12.07, −2.08]) ( 26 , 30 ), and post-breakfast (−8.6 mg/dL [−14.11, −3.09]) glucose ( 26 , 30 ). The soy protein–enriched diet had differences in change of HOMA-IR (mean −2.00 [95% CI −3.17, −0.83]) ( 27 ), required less medication use (RR 0.44 [95% CI 0.21, 0.91]), and had a lower birth weight (mean −184.67 g [95% CI −319.35, −49.98]) ( 14 , 27 ). The behavior modification diet had significant differences in change in postprandial glucose (mean −6.90 mg/dL [95% CI −9.85, −3.95]) and in HbA 1c (−0.19% [−0.26, −0.12]) ( 24 ) ( Table 3 ).

Assessment of Bias and Quality of the Evidence

None of the included studies were assessed as having a low risk of bias in all seven items of the Cochrane Collaboration tool ( Supplementary Fig. 2 ). Most studies were high risk for blinding of participants and personnel and for other sources of bias ( Supplementary Fig. 3 ). Studies scored high risk for other sources of bias for concerns such as baseline differences and industry funding. Most studies had an unclear risk of bias for selective outcome reporting and very few had registered protocols ( Supplementary Fig. 3 ).

GRADE assessment for the outcomes of interest reveals overall low to very low quality of evidence ( Supplementary Table 4 ). Considerations to downgrade quality of evidence involved the entire spectrum, including limitations in the study design, inconsistency in study results, and indirectness and imprecision in effect estimates.

Evaluation for Small Study Effect

Funnel plots of means and RRs of the primary outcomes for the main analysis are shown in Supplementary Figs. 4 and 5 and for the sensitivity analysis in Supplementary Figs. 6 and 7 . Overall, funnel plot asymmetry improves with the sensitivity analysis compared with the main analysis for neonatal birth weight outcomes.

In this meta-analysis, we pooled results from 18 studies including 1,151 women with a variety of modified dietary interventions. Remarkably, this is the first meta-analysis with a comprehensive analysis on maternal glucose parameters. Despite the heterogeneity between studies, we found a moderate effect of dietary interventions on maternal glycemic outcomes, including changes in fasting, post-breakfast, and postprandial glucose levels and need for medication treatment, and on neonatal birth weight. After removal of four studies with methodological concerns, we saw an attenuation of the treatment effect. Nonetheless, the change in post-breakfast and postprandial glucose levels and lowering of infant birth weight remained significant. Given the inconsistencies between the main and sensitivity analyses, we consider that conclusions should be drawn from the latter. These data suggest that dietary interventions modified above and beyond usual dietary advice for GDM have the potential to offer better maternal glycemic control and infant birth weight outcomes. However, the quality of evidence was judged as low to very low due to the limitations in the design of included studies, the inconsistency between their results, and the imprecision in their effect estimates.

Previous systematic reviews have focused on the easier-to-quantify outcomes, such as the decision to start additional pharmacotherapy and glucose-related variables at follow-up, but did not address change from baseline ( 15 – 17 ). The most recently published Cochrane systematic review by Han et al. ( 17 ) did not find any clear evidence of benefit other than a possible reduction in cesarean section associated with DASH diet. The very high-carbohydrate intake (∼400 g/day) and 12 servings of fruit and vegetables in the DASH diet ( 22 , 23 , 36 ) limit its clinical applicability and generalizability to women from lower socioeconomic, inner city backgrounds in Western countries. The Cochrane review shared one of our primary outcomes, large for gestational age ( 17 ). Neither meta-analysis detected a significant difference in risk of large for gestational age because the trials with a larger effect on birth weight (the three DASH studies) did not report on large for gestational age.

Our findings regarding pooled analysis of low-GI dietary interventions are broadly consistent with those of Viana et al. ( 16 ) and Wei et al. ( 15 ). Viana et al. ( 16 ) noted decreased birth weight and insulin use based on four studies of low-GI diet among 257 women (mean difference −161.9 g [95% CI −246.4, −77.4] and RR 0.767 [95% CI 0.597, 0.986], respectively). Wei et al. ( 15 ) also reported decreased risk of macrosomia with a low-GI diet in five studies of 302 women (RR 0.27 [95% CI 0.10, 0.71]). In our analyses of four studies in a comparable number of participants ( n = 276), we found the same direction of these effect estimates, without significant between-group differences. This is most likely due to the different studies included. For example, we were unable to obtain effect estimates stratified by type of diabetes in the study by Perichart-Perera et al. (which included women with type 2 diabetes) and therefore did not include this study ( 37 ). An important difference between our analyses and that of Wei et al. ( 15 ) is that they included DASH diet as a low-GI dietary subtype. We also included a recent study by Ma et al. ( 30 ) not included by the previous reviews.

Our sensitivity analyses highlighted concerns regarding some studies included in previous reviews. Notably, after removal of the studies with the most substantial methodological concerns in the sensitivity analysis, differences in the change in fasting plasma glucose were no longer significant. Although differences in the change in postprandial glucose and birth weight persisted, they were attenuated.

This review highlights limitations of the current literature examining dietary interventions in GDM. Most studies are too small to demonstrate significant differences in our primary outcomes. Seven studies had fewer than 50 participants and only two had more than 100 participants ( n = 125 and 150). The short duration of many dietary interventions and the late gestational age at which they were started ( 38 ) may also have limited their impact on glycemic and birth weight outcomes. Furthermore, we cannot conclude if the improvements in maternal glycemia and infant birth weight are due to reduced energy intake, improved nutrient quality, or specific changes in types of carbohydrate and/or protein.

We have not addressed the indirect modifications of nutrients. For example, reducing intake of dietary carbohydrates to decrease postprandial glucose may be compensated by a higher consumption of fat potentially leading to adverse effects on maternal insulin resistance and fetal body composition. Beneficial or adverse effects of other nutrients such as n-3 long-chain polyunsaturated fatty acid, vitamin D, iron, and selenium cannot be ruled out.

Our study has important strengths and weakness. To our knowledge, ours is the first systematic review of dietary interventions in GDM comprehensively examining the impact of diet on maternal glycemic outcomes assessing the change in fasting and postprandial glucose, HbA 1c , and HOMA-IR from baseline. This is especially important given that groups were not well balanced at baseline. Our review also benefits from the rigorous methodology used as well as the scientific, nutritional, and clinical expertise from an international interdisciplinary panel. However, it also has limitations. Baseline differences between groups in postprandial glucose may have influenced glucose-related outcomes. Furthermore, three of the included trials were pilot studies and therefore not designed to find between-group differences ( 12 , 26 , 34 ). The low number of studies reporting on adherence clearly illustrates that the quality of the evidence is far from ideal. The heterogeneity of the dietary interventions even within a specific type (varied macronutrient ratios, unknown micronutrient intake, and short length of some dietary interventions) and baseline characteristics of women included (such as prepregnancy BMI or ethnicity) may have also affected our pooled results. It should also be noted that the relatively small numbers of study participants limit between-diet comparisons. Last, we were unable to resolve queries regarding potential concerns for sources of bias because of lack of author response to our queries. We have addressed this by excluding these studies in the sensitivity analysis.

Modified dietary interventions favorably influenced outcomes related to maternal glycemia and birth weight. This indicates that there is room for improvement in usual dietary advice for women with GDM. Although the quality of the evidence in the scientific literature is low, our review highlights the key role of nutrition in the management of GDM and the potential for improvement if better recommendations based on adequately powered high-quality studies were developed. Given the prevalence of GDM, new studies designed to evaluate potential dietary interventions for these women should be based in larger study groups with appropriate statistical power. As most women with GDM are entering pregnancy with a high BMI, evidence-based recommendations regarding both dietary components and total energy intake are particularly important for overweight and obese women. The evaluation of nutrient quality, in addition to their quantity, as well as dietary patterns such as Mediterranean diet ( 39 ) would also be relevant. In particular, there is an urgent need for well-designed dietary intervention studies in the low- and middle-income countries where the global health consequences of GDM are greatest.

H.R.M. and R.C. contributed equally to this work.

See accompanying commentary, p. 1343 .

See accompanying articles, pp. 1337 , 1339 , 1362 , 1370 , 1378 , 1385 , 1391 , and e111 .

Funding. H.R.M. was funded by the U.K. National Institute for Health Research (CDF 2013-06-035). This work was conducted by an expert group of the European branch of the International Life Sciences Institute (ISLI Europe). This publication was coordinated by the ISLI Europe Early Nutrition and Long-Term Health and the Obesity and Diabetes task forces. Industry members of these task forces are listed on the ILSI Europe website at www.ilsi.eu . Experts are not paid for the time spent on this work; however, the nonindustry members within the expert group were offered support for travel and accommodation costs from the Early Nutrition and Long-Term Health and the Obesity and Diabetes task forces to attend meetings to discuss the manuscript and a small compensatory sum (honoraria) with the option to decline. The expert group carried out the work, i.e. collecting and analyzing data and information and writing the scientific paper, separate to other activities of the task forces. The research reported is the result of a scientific evaluation in line with ILSI Europe’s framework to provide a precompetitive setting for public-private partnership. ILSI Europe facilitated scientific meetings and coordinated the overall project management and administrative tasks relating to the completion of this work.

The opinions expressed herein and the conclusions of this publication are those of the authors and do not necessarily represent the views of ILSI Europe nor those of its member companies. For further information about ILSI Europe, please email [email protected] or call +32 2 771 00 14.

Duality of Interest. E.M.v.d.B. works part-time for Nutricia Research. E.C.-G. works full-time for Nestec. R.R. works full-time for Abbott Nutrition. No potential conflicts of interest relevant to this article were reported.

Author Contributions. J.M.Y. contributed to data extraction, statistical analyses, and writing the first draft manuscript. J.E.K. contributed to data extraction and writing the first draft summary tables. M.B. and A.G.-P. contributed to literature extraction, statistics, and manuscript revision. E.H. contributed to data extraction and GRADE assessments. I.S. and I.G. contributed to statistics and manuscript revision. E.M.v.d.B., E.C.-G., S.H., and S.F.O. contributed to concept and design, data extraction, and manuscript review. M.H. contributed to concept and design and draft manuscript evaluation. K.L. contributed to concept and design, data extraction, and critical review for intellectual content. L.P. contributed to concept and design and manuscript review. R.R., P.R., and H.R.M. contributed to concept and design, data extraction, and revising the draft manuscript. L.v.L. contributed to data extraction and draft summary tables. B.S. contributed to data extraction and critical review for intellectual content. R.C. contributed to literature extraction, statistical analyses, and revising the draft manuscript. R.C. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this work were presented at the Diabetes UK National Diabetes in Pregnancy Conference, Leeds, U.K., 14 November 2017, and the XXIX National Congress of the Spanish Society of Diabetes, Oviedo, Spain, 18–20 April 2018.

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Gestational Diabetes: A Review of the Current Literature and Guidelines

Hollander, Martine H. MD * ; Paarlberg, K Marieke MD, PhD † ; Huisjes, Anjoke J. M. MD †

*Resident, Department of Obstetrics and Gynecology, AZ Middelheim, Antwerpen, Belgium; and †Gynecologist, Department of Obstetrics and Gynecology, Gelre Ziekenhuizen (Hospitals), Apeldoorn, The Netherlands

CHIEF EDITOR'S NOTE: This article is part of a series of continuing education activities in this Journal through which a total of 36 AMA/PRA Category 1 Credits™ can be earned in 2007. Instructions for how CME credits can be earned appear on the last page of the Table of Contents.

The authors have disclosed that they have no financial relationships with or interests in any commercial companies pertaining to this educational activity.

Lippincott Continuing Medical Education Institute, Inc. has identified and resolved all faculty conflicts of interest regarding this educational activity.

Reprint requests to: Martine Hollander, Middelweg 19, 2312 KG Leiden, The Netherlands. E-mail: [email protected] .

Despite large numbers of original research studies spanning 4 decades there is still no consensus on the subject of gestational diabetes. Should all pregnant women be screened or only those with risk factors? Or is it safe not to screen at all? Which screening test and which diagnostic test are the most reliable? Which cutoff values should we use? What are the risks involved for mother and baby and can treatment improve outcome? What is the connection between gestational diabetes and diabetes mellitus type II? Are there disadvantages to screening? A review of relevant articles shows that definitive answers to these questions are not yet available. There is no gold standard screening test and no threshold glucose value above which complications are markedly increased. On the contrary, there appears to be a continuum of slowly increasing risks with rising blood glucose values, where it seems difficult to draw a clear line between pathology and physiology. Moreover, treatment has thus far not been shown to significantly improve outcome. There seems to be an indistinct area between the diagnosis of gestational diabetes and diabetes mellitus type II, where women with risk factors for one are also predisposed to develop the other, thereby confusing the diagnosis. Finally, the disadvantages to diagnosing and treating women without a clearly proven benefit seem to be significant. Therefore it seems defensible to suspend all screening and treatment for gestational diabetes, or at least significantly raise the threshold for making a positive diagnosis and initiating treatment, until further research has proven a clear benefit.

Target Audience: 

Obstetricians & Gynecologists, Family Physicians

Learning Objectives: 

After completion of this article, the reader should be able to summarize that there is still no worldwide consensus on the diagnosis, management, and adverse effects of Gestational Diabetes Mellitus (GDM); explain that all methods of screening vary in sensitivity and depend on very strict preparations for screening; state that there is no agreement on ideal levels of blood glucose to prevent untoward effects; and recall that there are two very large prospective studies that clarify the dark waters and that we should await their results.

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• Review Article
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• Published: 23 July 2024

Adherence to Mediterranean dietary pattern and the risk of gestational diabetes mellitus: a systematic review and meta-analysis of observational studies

• Saeede Jafari Nasab 1 ,
• Matin Ghanavati 2 ,
• Cain C. T.Clark 3 &
• Maryam Nasirian   ORCID: orcid.org/0000-0002-8365-3845 4 , 5  

Nutrition & Diabetes volume  14 , Article number:  55 ( 2024 ) Cite this article

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• Gestational diabetes

Background and aim

Gestational diabetes mellitus (GDM) is one of the most prevalent disorders occurring during pregnancy, which confers significant risk of short and long-term adverse outcomes in both mothers and offspring. Recently, more attention has been paid to the association of pre-pregnancy and early pregnancy healthy dietary patterns, such as Mediterranean dietary pattern with GDM. However, there is a lack of systematic review and meta-analysis summarizing findings in this regard. Hence, we sought to assess the association of MedDiet and GDM in observational studies by performing a systematic review and meta-analysis.

A comprehensive systematic literature search of observational studies was conducted via PubMed, Scopus, and Google Scholar, up to August 2023. Studies were included in our review if they evaluated the association of MedDiet and GDM, following an observational study design.

Ten studies were included in this study. Combining effect sizes, we found that adherence to MedDiet was inversely associated with GDM risk (OR = 0.64; CI: 0.52–0.78); implying that higher adherence to the MedDiet could reduce the risk of GDM by about 36%. Stratification by the geographic area, Mediterranean countries, time of dietary assessment and study design, showed a consistent significant association between MedDiet and GDM.

We conclude that adhering to diets resembling MedDiet, before or in early pregnancy, could be associated with lower risks or odds of GDM.

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Introduction.

Gestational diabetes mellitus (GDM) is one of the most prevalent disorders during pregnancy, which confers significant risk of short and long-term adverse outcomes in both mothers and their offspring [ 1 ]. The prevalence of GDM is rising worldwide, along with obesity, but its precise rate is unknown, and its range differs among countries from 2.5 to 14% [ 2 , 3 ]. Research has been conducted, primarily, on blood glucose control and medical and nutritional management of GDM, however, prevention of GDM by a healthy lifestyle and dietary pattern in pre-pregnancy or early pregnancy could be a better approach to improve the mother’s health and reduce the risk of birth defects and other diseases in children [ 4 , 5 ].

Empirical studies have suggested that lower consumption of fiber, polyunsaturated fatty acids, and low glycemic index foods, and higher intakes of carbohydrates, saturated fatty acids, cholesterol, iron, and total fat are associated with increased risk of GDM [ 6 ]. Although studying individual nutrients and food groups is helpful in understanding the underlying biological mechanisms, assessment of overall dietary patterns, such as Mediterranean dietary pattern (MedDiet), could be beneficial in better defining the association of diet and chronic disease, including GDM [ 7 ].

MedDiet is characterized by higher amounts of legumes, vegetables, whole grains, and foods rich in monounsaturated fatty acids (MUFA) and lower amounts of red and processed meat [ 8 ]. Recently, more attention has been paid to the association of pre-pregnancy healthy dietary patterns and GDM due to the inverse relationship of MedDiet with type 2 diabetes risk among non-pregnant individuals. Some studies have reported that adherence to MedDiet was associated with lower risks of GDM [ 5 , 7 , 9 ]. On the other hand, Parlapani et al. reported that adherence to MedDiet was not an independent predictor of GDM [ 10 ]. Li et al. revealed that Higher quartiles of alternate MED (AMED) scores were not associated with lower risk of GDM in week 16–22 and week 24–29 [ 11 ]. Moreover, one study revealed that when they evaluated the association of MedDiet and GDM using Mediterranean diet score (MDS), the results were significant, while they employed modified version of that scoring system the results were insignificant [ 12 ]. Thus, the results of these studies are somewhat equivocal. Moreover, there is a lack of systematic review and meta-analysis summarizing findings in this regard. Hence, we sought to assess the association of MedDiet and GDM in observational studies by performing a systematic review and meta-analysis.

This systematic review and meta-analysis study was conducted according to guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 13 ].

Search strategy

The primary electronic search was performed using PubMed, Scopus and Web of Science to find published observational studies, up to August 2023 (Supplementary Table 1 ). In this regard, the following text words and Medical Subject Headings (MeSH) related to Mediterranean dietary pattern and GDM were used: (“Mediterranean diet score” OR “Mediterranean diet” OR Mediterranean OR “dietary score” OR “dietary adherence” OR index-based OR “Diet, Mediterranean” OR “Mediterranean diet” OR “Med diet”) AND (“Gestational diabetes mellitus” OR GDM OR “diabetes pregnancy” OR “diabetic gestational” OR “gestational diabetes” OR “pregnancy induced diabetes”) AND (“Retrospective Studies” or “Cohort Studies” OR “prospective studies” Case-control OR cohort OR retrospective OR prospective OR cross-sectional OR nested OR longitudinal). There was not any restriction on time and language. Also, reference lists of studies were searched manually to avoid missing any potentially relevant publication. To perform the screening process, all searched studies were imported to EndNote library (version X9, for Windows, Thomson Reuters, Philadelphia, PA, USA). Duplicate citations were removed consequently.

Selection process

In the first step, two reviewers independently evaluated the eligibility of studies by screening titles, abstracts, and full texts of the articles, and any disagreements were resolved by consensus with a third researcher.

Inclusion criteria

Studies were included if they fulfilled the following criteria: (1) they examined the association of MedDiet and GDM in an observational study, (2) reported odds ratios (ORs) or relative risks (RRs) or hazard ratios (HRs), together with 95% confidence intervals (CIs), (3) Used valid methods for GDM diagnosis, such as glucose tolerance test (GTT), oral glucose tolerance test (OGTT) or Glucose challenge test (GCT).

Exclusion criteria

Studies were excluded if: (1) they were letters, reviews, meta-analyses, short communications, comments, ecological studies, and/or animal studies, (2) they contained unrelated content (3) they were published in non-English language.

Data extraction and synthesis

Two reviewers extracted the following data: (1) name of first author, (2) study name, (3) country, (4) study design, (5) outcome, (6) population size, (7) number of cases, (8) length of the study follow-up, (9) mean age or age range of study participants, (10) sex, (11) multivariable risk estimates (odds ratio (OR), risk ratio (RR) or hazard ratio (HR) comparing groups of highest and lowest adherence to MedDiet) with corresponding 95% confidence intervals (CI), (12) adjustment set, (13) methods used for dietary assessment and the diagnosis of GDM. If a study reported several risk estimates, the one with maximum adjustment was chosen. Sex-stratified or any other stratification for a variable was treated as two separate studies.

Study quality assessment

To define the quality of studies included in the meta-analysis, the Newcastle-Ottawa Scale was used [ 14 ]. Based on this scale, selection accounts for four stars, comparability for two and outcomes for three stars. The maximum star/score an observational study can get is 9, and studies that receive more than 6 stars may be defined as high quality.

Statistical analysis

To assess the association between adherence to MedDiet and GDM, DerSimonian and Laird random-effects models were used to calculate summary estimates of RRs, which considers between-study variations. Heterogeneity among studies was assessed using the I 2 index, where values more than 50% were considered as high heterogeneity [ 15 ]. In instances of high heterogeneity, sensitivity and subgroup analyses were used to identify the potential sources. Subgroup analysis was conducted according to the design of studies (cohort or case-control), geographical area of the study population (Mediterranean or non-Mediterranean), type of exposure of MedDiet (AMED or MED scores) and the period which considered as reference for dietary assessment (pre-pregnancy or pregnancy). Publication bias was assessed by Begg’s funnel plots and Egger’s regression test. All statistical analysis was performed using the software Comprehensive Meta-Analysis Software (CMA) and P values < 0.05 were considered as statistically significant.

Flow chart of article screening and selection process.

Figure 1 outlines the systematic search process of the study. A total of 180 publications were acquired from PubMed, Scopus, Google Scholar, and Web of Science, up to August 2023. After removing duplicated studies (n = 75) and excluding irrelevant studies after screening based on title and abstract (n = 64), 41 articles remained for further evaluation. Of the remaining publications, were excluded because they examined the association of dietary patterns and GDM through a posteriori method instead of a priori methods, 12 studies were excluded because of systematic review and meta-analysis design, 8 were excluded due to interventional design, 1 was excluded for not reporting OR/RR/HR effect sizes, and 1 was excluded due to multiple reports on the same data in separate studies. Finally, 10 eligible studies were included in the current meta-analysis: 2 case-control studies and 8 cohort studies.

Study characteristics and findings of studies

Main characteristics and findings of included studies are presented in Table 1 . They were published from 2012 to 2023, and the pooled sample size of included studies was 32959,909, with an age range of 18–45 years.

Among the included studies, three studies were conducted in the USA [ 5 , 11 ], one study in some Mediterranean countries (Algeria, France, Greece, Italy, Lebanon, Malta, Morocco, Serbia, Syria and Tunisia) [ 9 ], one study in Australia [ 7 ], one study in Iran [ 8 ], two studies in Spain [ 3 ], and two studies was conducted in Greece [ 10 ].

To assess adherence to Mediterranean dietary pattern, two studies used AMED [ 5 , 11 , 16 ], four studies used MED score [ 3 , 8 , 10 , 12 ], one study used MDI score [ 9 ], one study used MSDP score [ 7 ] and one study used Mediterranean Diet Adherence Screener (MEDAS) [ 17 ].

For exposure assessment, 8 studies used FFQ, one study used food record [ 8 ] and one did not mention the tool used for exposure assessment. To assess outcome (GDM), 3 studies used OGTT [ 7 , 9 , 11 ], three studies used National Diabetes Data Group criteria [ 3 , 5 , 17 ], 1 study used blood samples reports for fasting or postprandial blood sugar [ 8 ], one used oral glucose challenge test results using the Obstetricians and Gynecologists (HSOG) criteria [ 12 ] and two studies did not report the outcome assessment method [ 10 , 16 ].

Seven of ten studies showed that higher adherence to MedDiet was associated with lower risk of GDM [ 3 , 5 , 7 , 8 , 9 , 12 , 16 ] and 3 studies did not find any association between MedDiet and GDM [ 10 , 11 , 17 ].

The methodological quality of studies (Supplementary Table 2 ) was high in six publications [ 5 , 7 , 9 , 11 , 12 , 16 ] and moderate in four studies [ 3 , 8 , 10 , 17 ].

Meta-analysis findings

The pooled effect size of 10 studies indicated that there was a significant inverse association between MedDiet adherence and GDM (RR: 0.64; 95% CI: 0.52–0.78; p  < 0.001). The results displayed high heterogeneity between studies (I 2  = 75.35%, p  = 0.00). Results from the random-effects model are summarized in Fig. 2 .

Forest plot of the highest compared with the lowest categories of MED score and GDM risk for all included studies.

To ascertain the source of heterogeneity, subgroup analyses were conducted and presented in Fig. 3 . The inverse association was consistent across strata of geographic area (RR: 0.70; 95% CI: 0.53–0.91; I 2  = 68.78% for Mediterranean countries and RR: 0.56; 95% CI: 0.40–0.80; I 2  = 82.52% for non-Mediterranean countries), study design (RR: 0.74; 95% CI: 0.64–0.86; I 2  = 53.02% for cohort and RR: 0.25; 95% CI: 0.16–0.39; ; I 2  = 0% for case-control studies), type of MedDiet score (RR: 0.80; 95% CI: 0.68–0.93; I 2  = 51.58% for AMED and RR: 0.49; 95% CI: 0.34–0.72; I 2  = 70.78% for MED score) and the time period which considered as reference for dietary assessment (RR: 0.54; 95% CI: 0.38–0.76; I 2  = 84.20% for pregnancy and RR: 0.81; 95% CI: 0.73–0.91; I 2  = 0.00% for pre-pregnancy).

Forest plot for subgroup analysis of the association between MedDiet and GDM by geographic area ( A ), design of studies ( B ), MED score type ( C ) and period of dietary assessment ( D ).

Sensitivity analysis illustrated that overall effect size did not depend on a particular study (Supplementary Fig. 1 ). The Begg’s and Egger’s tests yielded coefficients of 0.62 and 0.02, respectively, indicating no evidence of publication bias. Furthermore, visual inspection of funnel plots in Fig. 4 showed a slight asymmetry for GDM.

Funnel plot showing study precision against the Odds ratio with 95% CIs for GDM.

The present study sought to review observational studies that investigated the association between MedDiet score and risk of gestational diabetes. In the pooled analysis of 10 studies, a significant association between adherence to MedDiet and lower risk of GDM was observed, with a heterogeneity of 75.35% ( p  < 0.001).

Subgroup analysis by geographic area indicated a significant reduction in GDM risk in studies conducted in both Mediterranean countries and non-Mediterranean countries. Although the association between adherence to MedDiet and lower risk of GDM remained significant across the study subgroups by the type of MedDiet, study design, period of dietary assessment (pre-pregnancy or during pregnancy) and countries, our results suggested that the observed heterogeneity between included studies may be attributed to type of study design or period of dietary assessment (Fig. 3B, D ). Pooled analysis of 2 case-control studies [ 3 , 8 ] included in this meta-analysis noted a significant reduction in odds of GDM, by 75%, among women with a high adherence to the MedDiet vs. with low adherence (RR: 0.25, 95% CI 0.16 to 0.39), whereas analysis of cohort studies indicated a moderate significant reduction in odds of GDM by 20% (RR: 0.80, 95% CI 0.72 to 0.89). This finding may be partially explained by retrospective nature of case-control designs which are prone to recall bias and are difficult to validate, thereby yielding a potential overestimation of the risk ratio [ 18 ]. It is worth mentioning that based on subgroup analysis both Mediterranean and non-Mediterranean population may benefit from adherence to a MedDiet, indicating mediterranean-based dietary recommendations could be applicable in both populations. Also, our results on association between MedDiet and risk of GDM remained significant after subgrouping based on timing of dietary assessment. However, cause of small number of studies and high percentages of heterogeneity between them, these results should interpret with caution.

The beneficial effects of adherence to MedDiet on the risk of chronic diseases including cancers [ 19 ], diabetes [ 20 ], and cardiovascular disease [ 21 , 22 ] has been evidenced in recent studies. High consumption of plant-based foods, especially whole grain products, vegetables, fruits, nuts, extra virgin olive oil, and legumes with regular intake of fish and seafood are characteristics of a typical MedDiet [ 23 ]. Since oxidative stress and systemic inflammation are important contributing factors in the development and progression of chronic disease, the high content of antioxidants and vitamins found in MedDiet can explain potential benefits of adherence to MedDiet on the risk of chronic diseases [ 24 ].

Overweight and obesity, maternal age, family history, or any form of diabetes and insulin resistance are the most common risk factors for GDM [ 25 ]; among them, obesity and insulin resistance have inversely related with Mediterranean diet. Accordingly, a meta-analysis of 6 cohorts indicated that greater adherence to the Mediterranean diet was associated with a 9% lower risk of being overweight or obese [ 26 ]. Papadaki and colleagues, in a systematic review and meta-analysis of randomized control trials (RCTs), showed beneficial effects of MedDiet on a multitude of outcomes related to metabolic health, including insulin resistance [ 27 ]. The high content of fiber, functional foods, and polyphenols found in MedDiet has previously been proposed to attenuate central obesity and inflammation status and their consequence insulin resistance, which might elucidate its favorable effects [ 28 , 29 ].

To date, several components of the Mediterranean diet pattern have been reported to be associated with lower risk of GDM. Considerable amount of polyphenols in fruits and vegetables is purported to reduce risk of GDM via several mechanisms, including increased antioxidant capacity, anti-inflammatory effects, inhibition of glucose absorption in the gastro-intestinal tract, and microbiota modification [ 30 ]. In addition, regular consumption of vegetables rich in fiber can result in weight loss in obese individuals, potentially negating obesity as the most modifiable risk factor for GDM [ 31 ]. With respect to whole grains, it is now fully evidenced that total whole grain consumption is associated with a lower risk of type 2 diabetes [ 32 , 33 ]. A potential diabetes-protective effect of nuts, as an important component of the Mediterranean pattern, has been illustrated in a number of studies [ 34 , 35 ]. The therapeutic benefits of nuts may be attributable to their nutritional components and bioactive substances. Nuts include monounsaturated and polyunsaturated fatty acids, which may have a role in glucose regulation and appetite reduction. By modifying gut microbiota, fiber and polyphenols in nuts may also have an anti-diabetic impact [ 36 ]. Pang et al. [ 37 ], in a cohort study, concluded that soy-based foods and nuts consumption during early pregnancy could independently result in a significant reduction in odds of GDM. Although fish contains n-3 Polyunsaturated Fatty Acid, its preventive effects on diabetes in epidemiological evidence remains elusive [ 38 ]. It seems that the benefits of fish consumption are additive with other foods when consumed in context of a healthy dietary pattern, such as MedDiet.

Additionally, MedDiet also includes low to moderate intake of dairy products, eggs and poultry, moderate intake of alcohol, and low intake of red meat and sweets as detrimental components of the diet [ 23 ]. Results from observational studies suggest a significant association between long term intake of red meat and increased GDM risk [ 39 , 40 ]. Although the mechanism by which high intake of red meat can affect GDM risk are not fully understood, high content of cholesterol and saturated fatty acid found in meat may be related to a progressive loss of beta-cell function [ 41 ]. In connection with dairy products, despite having high content of calcium, magnesium, vitamin D, and whey proteins, which has been claimed to mitigate body fat and insulin resistance [ 42 ], both low-fat and high fat dairy products consumption have been reported to be ineffective in reducing risk of diabetes [ 43 , 44 , 45 , 46 ].

It is worth noting that the Mediterranean diet approach is largely based on plant-based foods, but recommendation for regular and moderate consumption of low-fat dairy products in the MedDiet helps individuals to provide essential amino acids, which are limited in plant foods. A contentious component of a MedDiet is ethanol, which is typically represented by red wine. Among included studies in this meta-analysis, 2 studies did not include alcohol consumption in calculating Med score, because of zero intake of alcohol in the majority of participants [ 9 ] or its controversial effects on pregnancy outcomes [ 11 ], and in 2 studies, there was no information regarding alcohol beverage consumption [ 8 , 10 ]. Although red wine contains a number of potential protective ingredients, its overall effects on adverse pregnancy outcomes remains unclear [ 47 ].

Regarding the period of dietary assessment, the results of subgroup analysis showed a negative association between adherence to MedDiet and risk of GDM in both pre and during pregnancy (Fig. 3 ). Among the 10 included studies, six studies assessed the adherence to MedDiet during pregnancy and 4 studies before pregnancy. In accordance with the finding of our study, several studies confirmed the association between the adherence to MedDiet before gestation [ 5 , 7 ] or during pregnancy [ 9 , 11 ] and the lower risk of GDM. Taken together, the documented advantages of a MedDiet are most likely not attributable to the isolated impact of a single component, but rather to the synergistic effects and intricate interactions of all the diets’ constituents.

To the best of our knowledge, this is the first systematic review and meta-analysis investigating the association between adherence to MedDiet and risk of GDM. Our study has some strengths, including almost all included studies in this meta-analysis used the same method to assess adherence to MedDiet [ 48 ], no single study seemed to have a considerable effect on heterogeneity based on sensitivity analysis, and the food frequency questionnaires used in these studies have been validated and shown to be a valuable tool for assessing habitual dietary intake. Also, high methodological quality among included studies must be considered a strength of this study. Also, to detect the source of observed heterogeneity, subgroup analysis was conducted. However, some limitations are unavoidable and should be noted. In a few studies, some components of the MedDiet were not taken into account for measuring MedDiet score, owing to lack of data. Moreover, eight of included studies used FFQ as dietary assessment tool, one study food record and another one did not mention which tools was utilized. Although all dietary assessment techniques are prone to both random and systematic measurement error, their value for research, monitoring, and policy settings is not diminished by this. Also, the low number of well-designed studies with large populations investigating the association between MedDiet and GDM is another limitation that should be addressed in future research.

In conclusion, this systematic review and meta-analyses presented additional evidence indicating a favorable effect of high adherence to MedDiet on risk of GDM. Our findings support the protective effect of adherence to a MD pattern prior to pregnancy and during pregnancy on adverse pregnancy outcomes, like GDM. Considering the relation of GDM with future complications in mothers and their children, findings of this study support implementing the MedDiet in women of reproductive age and even during the pregnancy to reduce the risk of GDM and consequent adverse outcomes. Thus, including the MedDiet pattern recommendations in public health programs could yield benefits for both women and health care system. However, future well-designed interventional studies with adequate population are needed to strengthen our findings. For instance, there are limited RCTs investigating the effect of MedDiet (excluding alcoholic beverages) in first trimester of pregnancy on adverse pregnancy outcome including GDM. Moreover, exploring the effect of MedDiet effects on adverse outcome such as GDM in high-risk groups such as women with over weight and obesity pre- and during pregnancy could be beneficial. Lastly, further prospective studies on the interaction of MedDiet, genetic and lifestyle risk factor of GDM are warranted.

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Department of Clinical Nutrition, School of Nutrition and Food Sciences, Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Saeede Jafari Nasab

National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Matin Ghanavati

Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5FB, UK

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Infectious Diseases and Tropical Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

Maryam Nasirian

Epidemiology and Biostatistics Department, Health School, Isfahan University of Medical Sciences, Isfahan, Iran

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SJN AND MG contributed to systematic search, screening and data extraction. SJN performed the analysis and designed the figures. SJN wrote the manuscript with support from MP and MG. CCTC reviewed the paper and revised it to the final format. MN supervised the project. All authors read an approved the final manuscript.

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Jafari Nasab, S., Ghanavati, M., C. T.Clark, C. et al. Adherence to Mediterranean dietary pattern and the risk of gestational diabetes mellitus: a systematic review and meta-analysis of observational studies. Nutr. Diabetes 14 , 55 (2024). https://doi.org/10.1038/s41387-024-00313-2

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

Affiliation.

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

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

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

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Gestational Diabetes Mellitus and Macrosomia: A Literature Review

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• DOI: 10.4103/singaporemedj.SMJ-2023-136
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Barriers to postpartum diabetes mellitus screening among mothers with a recent history of gestational diabetes mellitus: a cross-sectional study.

• Y. S. Andrew Tan , L. P. M. Tsang , +6 authors N. Tan
• Published in Singapore medical journal 15 July 2024

18 References

The impact on postpartum care by telehealth: a retrospective cohort study., bridging the postpartum gap: a randomized controlled trial to improve postpartum visit attendance among low-income women with limited english proficiency, health literacy and diabetes knowledge: a nationwide survey in a multi-ethnic population, facilitators and barriers to post-partum diabetes screening among mothers with a history of gestational diabetes mellitus–a qualitative study from singapore, comprehensively addressing postpartum maternal health: a content and image review of commercially available mobile health apps, improving uptake of postnatal checking of blood glucose in women who had gestational diabetes mellitus in universal healthcare settings: a systematic review, evaluation of neonatal and maternal morbidity in mothers with gestational diabetes: a population-based study, about predictions in spatial autoregressive models: optimal and almost optimal strategies, early screening for type 2 diabetes following gestational diabetes mellitus in france: hardly any impact of the 2010 guidelines, postnatal gestational diabetes mellitus follow-up: australian women's experiences., related papers.

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The relationship between quality of life and some mental problems in women with gestational diabetes mellitus (GDM): a cross-sectional study

• Soheila Nazarpour 1 ,
• Masoumeh Simbar 2 , 3 ,
• Zahra Kiani 2 ,
• Neda Khalaji 3 ,
• Mobina Khorrami Khargh 3 &
• Zahra Naeiji 4  

BMC Psychiatry volume  24 , Article number:  511 ( 2024 ) Cite this article

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Women with medical problems during pregnancy, including women with Gestational Diabetes Mellitus (GDM), experience an increased prevalence of mental health disorders which can affect their quality of life. This study aimed to assess the relationship between GDM-related quality of life and depression, anxiety, and stress.

This analytical cross-sectional study was performed on 150 women with GDM. The participants were selected using a multi-stage sampling including quota and then randomized method from maternal care centers affiliated with Shahid Beheshti University of Medical Sciences, Tehran-Iran. The data were collected using a personal information questionnaire, the GDM-related quality of life questionnaire (GDMQoL-36), and the depression, anxiety, and stress scale (DASS). The data were analyzed using SPSS-23 software and statistical tests of coefficient Spearman’s correlation, t -test, analysis of variance, and multiple linear regression.

The mean ± SD score for the GDM-related quality of life and the DASS scale were 55.51 ± 8.87 and 27.12 ± 19.43%, respectively. Different degrees of depression, anxiety, and stress were present in 40, 61.3, and 42% of women, respectively. The total score of GDM-related quality of life had a significant negative correlation with the total score of DASS and the scores of the subscales including depression, anxiety, and stress ( P  < 0.001). There were significant correlations between the total score of GDM-related quality of life with age, BMI, length of marriage, educational level of the woman and her spouse, the occupation of the woman and her spouse, income, and economic class of the family. Multiple linear regression revealed that depression, education, and job are predictive factors for GDM-related quality of life.

GDM-related quality of life is related to some mental disorders. Therefore, it is important to consider the mental health promotion of pregnant women with GDM in future prenatal health programs to improve their quality of life. This also shows the importance of integrating mental health promotion strategies to enhance the quality of life of pregnant women with GDM.

Peer Review reports

Introduction

Gestational diabetes Mellitus (GDM) is the most common medical complication of pregnancy [ 1 ]. According to the 2024 American Diabetes Association (ADA), Gestational diabetes mellitus is defined as diabetes diagnosed in the second or third trimester of pregnancy that was not overt diabetes before gestation or other types of diabetes occurring throughout pregnancy, such as type 1 diabetes. According to the ADA report, type 1 diabetes is caused by autoimmune beta-cell destruction and usually leads to absolute insulin deficiency in adults, and type 2 diabetes is caused by a non-autoimmune progressive loss of adequate β-cell insulin secretion, frequently on the background of insulin resistance and metabolic syndrome [ 2 ].

This disease is usually diagnosed in weeks 24 to 28 [ 3 ]. The global prevalence of GDM is estimated at 10.13% and evidence suggests that diabetes in all forms, especially gestational diabetes, is increasing as one of the main metabolic disorders in pregnancy [ 4 , 5 ]. So, GDM is known as one of the fastest-growing forms of diabetes due to the increase in obesity rates and maternal age worldwide [ 6 ]. The latest documents from the International Diabetes Federation show that in 2021, 16.7% (1 in 6) of live births worldwide were affected by maternal hyperglycemia during pregnancy, 80.3% of which were due to GDM [ 7 , 8 , 9 ].

This disease affects approximately 6% of pregnancies in Iran, with an estimated prevalence of 1.3 to 18.6% [ 10 ]. In a meta-analysis in Iran, the overall prevalence of GDM in 2015 was estimated at 3.4% [ 11 ].

GDM is associated with adverse maternal outcomes such as increased risk of cesarean section, preeclampsia, develop type 2 diabetes, cardiovascular disease, malignant tumors, ophthalmic diseases.

renal disease, dyslipidemia and postpartum metabolic disorders; and fetal outcomes including increasing the risk of LGA and macrosomia, shoulder dystocia, preterm labor, stillbirth, infant mortality, neonatal complications, fetal hyperglycemia, hyperbilirubinemia, neonatal respiratory distress syndrome, impaired neurodevelopment, increased risk of type 2 diabetes, obesity, cardiovascular disease, and mental disorders [ 1 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. Maternal blood glucose transfers to the fetus through the placental circulation and causes fetal hyperglycemia [ 20 ], which may lead to LGA and macrosomia. Fetal hyperglycemia causes fetal tissue disproportion, including increased fetal fat tissue, thickening of the skin folds, and increased shoulder-to-head ratio. Therefore, these infants are at risk for dystocia and shoulder fractures due to the anthropometric alterations [ 15 ]. Besides, increased rates of preterm labor and cesarean section are from other important complications of GDM, which can lead to stillbirth and infant mortality [ 16 ].

Babies born neonates of women with GDM are at high risk of hypoglycemia, hyperbilirubinemia, and neonatal respiratory distress syndrome, as well as prolonged length of stay in the neonatal intensive care unit [ 19 ]. In addition, these infants are also at risk for poor long-term health outcomes, including impaired neurodevelopment, difficulty maintaining a normal body mass index (BMI), and increased risk of type 2 diabetes, obesity, cardiovascular disease, and mental disorders [ 17 ]. Compared to women with normal pregnancies, pregnant women with GDM are more likely to develop type 2 diabetes [ 21 ], cardiovascular disease, malignant tumors, kidney disease and ophthalmic diseases, dyslipidemia, and postpartum metabolic disorders [ 12 , 13 , 14 , 17 , 18 ].

This clinical condition potentially has a negative effect not only on medical outcomes but like other chronic diseases, it can negatively affect almost all aspects of the patient’s life. It often leads to the deterioration of the patient’s physical and mental health, changes in lifestyle and adaptation to the disease, as well as changes in physical, professional, and social activities, as well as values. All this also affects the patient’s quality of life [ 22 , 23 , 24 , 25 ].

The risks and adverse consequences mentioned above force pregnant women with GDM not only to bear the physical and mental discomfort of the disease but also to worry about the safety and prognosis of the fetus. In addition, the behavioral restrictions caused by the disease have effects on the social activities and work life of these pregnant women, and the costs of treating the disease increase the economic burden of their families to different degrees [ 26 ]. All these results seriously affect the quality of life of pregnant women with GDM [ 17 , 18 , 19 , 26 , 27 , 28 , 29 ].

Mental disorders of pregnant women, especially the psychological condition of women with GDM, as a high-risk group, attracted much attention from researchers around the world. Studies conducted in this population indicate that apart from physiological factors, anxiety and depression are also associated with GDM [ 27 ]. Evidence suggests that there may be a bidirectional relationship between gestational diabetes and anxiety and depression [ 27 ]. Anxiety and depression can lead to chronic hypothalamic-pituitary-adrenal hyperactivity, which leads to increased cortisol secretion and insulin resistance [ 30 ] and increased risk of GDM in pregnant women. On the other hand, these patients face many obstacles and challenges, such as mental stress, fear of disease, and worries during pregnancy, and they feel more anxious about the possibility of developing diabetes and their neonatal health (the effect of insulin or diet on the fetus) [ 27 , 31 ]. A possible physiologic mechanism for this significant association could be linked to the secretion of cortisol and expression of certain inflammation markers in pregnancy, which are in turn associated with hyperglycemia and insulin resistance [ 32 ]. At the same time, a diagnosis of GDM may increase the risk of antepartum or postpartum depression through a reverse mechanism [ 33 ]. Several studies have shown that women with medical problems during pregnancy, including women with GDM, report higher levels of symptoms of depression, anxiety, and stress compared to women without complications [ 27 , 34 , 35 , 36 , 37 , 38 , 39 ]. In a study on 526 women with GDM in Malaysia, it was found that among women with GDM, the prevalence of anxiety symptoms was the highest (39.9%), followed by depressive symptoms (12.5%) and stress symptoms (10.6%) [ 40 ]. Also, Hinkle et al.‘s study showed that the probability of depression in women with GDM is 2 to 4 times higher than pregnant women without GDM [ 34 ].

Quality of life (QoL) is the most important concept studied in health care research. With the increase in life expectancy and the prevalence of chronic diseases, there is a need to pay attention to the quality of life. Assessment of quality of life helps to improve the health status of patients and the quality of care provision [ 41 ]. Quality of life therapy empowers people to actualize their knowledge, attitudes and values [ 42 ].

In recent years, healthcare and clinical researchers have concentrated on the concept of quality of life to assess the healthcare challenges in chronic diseases [ 43 , 44 ]. Quality of Life is defined as an individual’s perception of their position in life in the context of the culture and value systems in which they live and about their goals, expectations, standards, and concerns [ 45 ]. GDM also has physical, social, mental, and psychological consequences that can affect the quality of life of women [ 46 ]. Thus, increasing the quality of life through reasonable interventions is considered as important as metabolic control and prevention of complications in GDM care and treatment procedures. Therefore, quality of life assessment should be implemented as a clinical standard in GDM care [ 44 ].

The quality of life of women with GDM is affected by several factors [ 29 ]. Today, there is increasing attention to the quality of life, and researchers investigated many factors to identify the effective factors. According to studies, several factors affect the quality of life of women with GDM. Individual-specific variables including demographic variables such as age [ 43 ], level of education and BMI [ 47 ], variables related to pregnancy and disease, social factors, and psychological factors are important factors that affect the quality of life of pregnant women with GDM [ 29 , 43 ]. Undoubtedly, identifying the factors affecting the quality of life in diabetic patients improves the health of patients and increases their survival [ 43 ]. An important group of these factors are mental disorders because GDM can have negative effects on maternal mental health and thereby affect the quality of life [ 25 ]. Therefore, this study aimed to assess the relationship between depression, anxiety, and stress with the quality of life of women with GDM.

Study design

This was a correlational cross-sectional study.

The participants

The samples were 150 pregnant women affected by GDM and referred to the prenatal care clinics of the hospitals affiliated with Shahid Beheshti University of Medical Sciences (SBMU), Tehran-Iran.

Inclusion criteria included diagnosis of with at least one abnormal value ≥ 92, 180, and 153 mg/dl for fasting, one-hour, and two-hour plasma glucose concentration respectively, after a 75 g oral glucose tolerance test in 24–28 weeks of pregnancy [ 48 , 49 ]. The exclusion criteria were the incomplete responses to the questionnaires. However, there was no missing in sampling because the information was collected by Google form, and answering the questions was mandatory, so the questionnaires could not be submitted without responding to all questions.

A multi-stage sampling including quota and then randomized method was used to recruit the subjects of the study. Firstly, four hospitals in the north, south, east, and west of Tehran affiliated with SBMU were selected. Then, quota sampling was used to recruit samples from the prenatal care clinics of the selected hospitals including Taleghani, Mahdieh, Emam-hossein, and Shohada. Hospitals. Following the total sample size calculation, the number of participants was distributed based on the monthly average number of clients with GDM who were visited in each clinic. At that time, the samples were randomized using the Excel random selection option from the women with the eligibility criteria. Then they were informed about the objectives of the study and signed the electronic informed consent form before completing the questionnaires, and, finally, the questionnaires were completed electronically by the participants.

The number of samples for the study was calculated at 146 using the following formula. The total sample size N = [(Zα + Zβ)/C]2 + 3 = 146, considering α (two-tailed) = 0.05, β = 0. 20 and r  = 0.23 (stress and quality of life) [ 25 , 43 ] and therefore considering the standard normal deviate for α = Zα = 1.96, the standard normal deviate for β = Zβ = 0.84 and C = 0.5 * ln[(1 + r)/(1-r)] = 0.23.

Tools for data collection

The tools for data collection were 3 questionnaires including a personal information questionnaire, a valid and reliable questionnaire to assess Quality of life in Gestational Diabetes Miletus (GDMQoL-36) designed by Mokhleshi et al. [ 50 ], and the Depression, anxiety, and stress scale (DASS) questionnaire [ 51 ].

The personal information questionnaire

The questionnaire Contains items related to socio-demographic and fertility information. It included 21 questions about the participant’s age, education, income, employment status weight, height, duration of the marriage, gravida, parity, abortion, unwanted or unwanted pregnancy, desired sex of the fetus, gestational age, as well as the history of gestational diabetes, history of preterm labor and history of stillbirth, and also about the GDM and the treatment protocols. All the questionnaires were prepared as the Google form and were electronically filled up after giving informed consent.

Gestational diabetes miletus-related quality of life questionnaire (GDMQoL-36)

GDMQoL-36 is developed to assess the quality of life of women with GDM. It consists of 36 questions in 5 domains (concerns about high-risk pregnancy, perceived constraints, complications of GDM, medication and treatment, and support).

The items in the domains of concerns about high-risk pregnancy, perceived constraints, complications of GDM, and medication and treatment are scored by a 5-point Likert scale 1 to 5 strongly agree to strongly disagree). There was an exception for item 30, “I adjust insulin dose based on my blood glucose” which is scored 5 to 1 for strongly agree to strongly disagree. In the domain of support, the answers are scored 5 to 1 for strongly agree to strongly disagree. For the participants who do not receive Insulin, the scores of these questions are considered 3 (Neutral).

The total score of the instrument is computed by calculating the average of the total modified scores of the instrument. The total score of the questionnaire, based on the above explanations is 36–180, with higher scores representing higher quality of life. Because of the diversity of the domains and the scales, a standard 0 to 100 method of scoring was used for better understanding and comparison of the scores of the domains. To convert the scores from 0 to 100, the following formula was used. Adjusted score = (raw score-minimum /maximum -Minimum) *100.

GDMQoL-36 is a standard questionnaire with S-CVI and S-CVR 0.99 and 0.73, respectively. Factor analysis using varimax rotation indicated that the 5 factors can explain 46.68% of the variance. Also, a significant convergent validity was demonstrated between GDMQoL-36 and the “Diabetes Clients Quality of Life questionnaire” (DCQOL) ( r  = 0.64) [ 52 ]. The internal consistency of the GDMQoL-36 was shown by Cronbach’s alpha 0.93 and its test-retest stability was demonstrated by an intra-class correlation coefficient of 0.95 [ 50 ]. This questionnaire is developed and psychometrically assessed in Iran.

Depression, anxiety, and stress scale questionnaire (DASS)

The Depression Anxiety Stress Scale 21 (DASS-21) is a short form of Lovibond and Lovibond’s (1995) 42-item measure of depression, anxiety, and stress (DASS) [ 51 ]. The shortened 21-item scale performs as well as the 41-item scale and is considered the preferred version of the scale [ 53 , 54 ]. The DASS-21 questionnaire includes 3 subscales and contains 21 questions and evaluates depression, anxiety, and stress with 7 questions for each subscale, and scoring based on a 4-point scale from 0 to 3. The final score of each subscale is obtained through the sum of the scores of the related questions. The items 1, 6, 8, 11, 12, 14, and 18 measure stress, and the items 3, 5, 10, 13, 16, 17, and 21 assess depression, and also the items 2, 4, 7, 9, 15, 19, and 20 measure anxiety. The scores range from 0 to 21 for each subscale, and the total score ranges from 0 to 63. A higher score indicates more depression, anxiety, and stress. The scoring method is that each question is considered from 0 (does not apply to me at all) to 3 (completely applies to me). Since DASS-21 is the shortened form of the main scale (42 questions), the final score, for each subscale should be doubled (the total score is between 0 and 126 and the score of each subscale is between 0 and 42) [ 51 , 55 , 56 ]. Then, the responders are classified into normal, mild, moderate, severe, and very severe groups, based on the scoring results and according to Table  1 [ 57 ].

The validity of the short form of DASS-21 was evaluated by Crawford and Henry. The reliability of the DASS-21 was confirmed by Cronbach’s alpha at 0.88 for depression, 0.82 for anxiety, 0.90 for stress, and 0.93 for the total scale [ 54 ].

The Persian version of the questionnaire was validated by Sahibi et al. and the internal consistency of the test was determined to be satisfactory and was almost equal to the internal consistency of the original version of the DASS created by Lavibond and Lavibond in 1995. In the study of Sahebi et al., the internal consistency of the DASS scales was calculated using Cronbach’s alpha at 0.77 for the depression subscale, 0.79 for the anxiety subscale, and 0.78 for the stress subscales [ 58 ]. The Persian version was used in this research.

Statistical analysis

The data was extracted from the Google form in the Excel software that was converted to SPSS-23. The normality of the variables was examined using the Kolmogorov-Smirnov test. Then, the data were analyzed using t-test, ANOVA, Spearman correlation tests, and linear multiple regression analysis. Multiple linear regression was performed by a backward stepwise method. P values less than 0.05 were considered significant.

One-hundred fifty women with GDM with an average age of 31.44 ± 6.64 (Mean ± SD) years and gestational age of 30.77 ± 6.09 weeks participated in this study. Among these women, 45.3% had a history of GDM in a previous pregnancy, and 50% of them used insulin. The sociodemographic characteristics of women are presented in Table  2 .

The total score for GDMQoL-36 was 55.51 ± 8.87% (Mean ± SD). The highest score was related to the domain of support (72.53 ± 16.51%) and the lowest score belonged to Perceived constraints (39.06 ± 20.76%) (Table  3 ).

The total score for the DASS-21 scale was 27.12 ± 19.43%. The scores for the depression, anxiety, and stress subscales were 22.51 ± 22.05, 23.17 ± 16.93, and 35.68 ± 23.82%, respectively. Findings revealed that the participants are experiencing different degrees of depression (40%), anxiety (61.3%), and stress (42%) (Table  4 ).

The correlations between the total score of GDMQoL-36, with the total score of DASS-21, and its subscales are shown in Table  5 . As the table shows, there were significant negative correlations between the total score of GDMQoL-36, with the total score of DASS-21, and depression, anxiety, and stress subscales ( P  < 0.001).,

Also, the score of Perceived constraints, Complications of GDM, and Medication and treatment dimensions of quality of life had a significant negative correlation with the total score of DASS-21 and the scores of depression, anxiety, and stress.

The relationships between GDMQoL-36 and the sociodemographic characteristics are shown in Table  6 . The results showed significant negative correlations between age ( P  = 0.016), BMI ( P  = 0.005), and length of marriage ( P  = 0.045) with the total score of GDMQoL.

Also, the ANOVA test revealed a significant relationship between GDMQoL, with the women’s education and their spouse’s education ( P  < 0.001). Also, Tamhane’s T2 post hoc test showed that the score GDMQoL is higher among participants with higher education ( P  < 0.05).

Independent t -tests showed that working women had a higher GDMQoL score than housewives ( P  = 0.007). Also, the ANOVA test disclosed a significant difference in the scores of GDMQoL based on the husband’s occupation ( P  = 0.007) and Scheffe’s post hoc test revealed that the GDMQoL score was higher in women whose husbands were employees than in the women whose husbands were workers ( P  < 0.05).

Performing the t -test showed that women with GDM with sufficient family income had a higher GDMQoL score than women with insufficient family income ( P  = 0.001). Also, the ANOVA test showed a significant difference in GDMQoL based on the economic class of the family ( P  = 0.001) and Scheffe’s post hoc test revealed more GDMQoL scores in women with high economic class compared to the two groups with Middle and low economic class ( P  < 0.05). The rest of the sociodemographic variables did not show a significant relationship with the GDMQoL.

The assumption for the multiple linear regression model was that GDMQoL-36 was related to depression, anxiety, and stress. In multiple linear regression, the GDMQoL-36 score was considered the dependent variable, and scores of DASS were the main variables whose relation to the GDMQoL-36 score was measured. Age, BMI, duration of marriage, education (women and husband), occupation (women and husband), income, and economic class were included in regression models by stepwise method, as they were considered potential confounding variables. In our regression analyses, the R 2  = 0.372, which showed that 37.2% of the outcome variable (score of GDMQoL-36) was explained by the variables included in the regression model. The interactions between confounding variables were assessed. However, these interaction terms were not included in the final model as they were not statistically significant.

The results of multiple linear regression based on the Stepwise method showed that depression, education, and occupation are predictive factors for the total GDMQoL score. So for each unit increase in depression score, the total GDMQoL score decreases by 0.689 units ( P  < 0.001). Also, the total GDMQoL score in working women is 4.233 higher than that of housewives ( P  = 0.022). Also, with an increase in education level, the total GDMQoL score increases by 4.872 ( P  < 0.001) (Table  7 ).

This study showed there is a strong negative correlation between GDM-related quality of life (GDMQoL) with mental disorders including depression, anxiety, and stress. Among mental disorders, depression is a predictor of the GDMQoL. Stress, depression, and anxiety are the most important psychological reactions of an individual who is diagnosed with a new disease such as gestational diabetes [ 59 ]. Some studies also showed the significant role of intervening psychosocial factors, such as depression and stress in the quality of life of diabetic patients [ 43 , 60 ]. QOL of women with GDM had been severely affected by concerns about a high-risk pregnancy [ 46 ].

The finding demonstrated a strong significant negative correlation between GDMQoL and depression, and depression was a predictor for the total score for GDMQoL, so for each unit increase in depression score, the total GDMQoL score decreased by 0.689 units. This result is inconsistent with other studies [ 27 , 29 , 61 , 62 , 63 , 64 ]. Depression is a common complication in the perinatal period which is associated with an increased risk of adverse pregnancy outcomes [ 65 , 66 ]. In a systematic review, OuYang et al. showed that depression is a risk factor for poor quality of life in pregnant women with GDM [ 27 ]. Depression not only leads to hormonal imbalance and increased blood sugar in pregnant women but also increases the incidence of cesarean section and adverse maternal-fetal/neonatal consequences [ 27 ]. The higher prevalence of depressive symptoms in women with GDM may be related to a less healthy lifestyle in these women [ 62 ]. The consequences of prenatal depression are not limited to pregnancy and childbirth itself, but may also have postnatal significant negative outcomes [ 62 ]. Depressed women with GDM decrease the use of social support and they have serious concerns about the disease and treatment, which in turn increases the development of depression, and forms a vicious circle of further decreasing quality of life [ 63 ]. In a study on 1843 Belgian women, Minschart and colleagues found that women with prenatal depressive symptoms are more likely to develop GDM, and these women often remain depressed during the postpartum period and have a lower quality of life [ 62 ].

The present study showed a strong significant negative correlation between GDMQoL and anxiety. This result is consistent with some studies [ 61 , 63 , 67 ]. Mokhlesi and colleagues showed that worry during pregnancy was significantly higher in women with GDM compared to low-risk pregnant women. Among the different dimensions of GDMQoL, concerns about high-risk pregnancy, such as concerns about childbirth and the neonate, are the most critical issues in gestational diabetes [ 31 ] and the strongest predictor of their quality of life [ 50 ]. Women reported concerns about their fetus and neonate health, preterm labor and reduced fetal movements, intrauterine growth restriction, and stillbirth due to GDM [ 31 ]. These concerns cause anxiety that affects mental health and the quality of life [ 39 ]. In a study on 526 pregnant women with GDM in Malaysia, Lee et al. indicated that women with a family history of depression or anxiety compared to those who did not have this history were more likely to suffer from poor to moderate quality of life [ 61 ].

The results showed a strong significant negative correlation between the scores for GDMQoL and stress. The relationship between stress and low quality of life is demonstrated in the study of Long and colleagues on 465 Chinese women with a history of GDM [ 35 ]. Indeed, pregnancy is a stressful condition for women which can be exacerbated by high-risk pregnancy [ 31 , 68 ]. GDM diagnosis is usually unexpected and may increase negative experiences and perceptions during pregnancy [ 59 ]. Furthermore, the quality of life of women with GDM may be affected by concerns about maternal and fetal/child health, as well as by the feeling of losing control over their health [ 69 , 70 ]. Frequent metabolic changes caused by GDM require regular visits to doctors and medical treatment, which can cause stress in sensitive situations such as pregnancy [ 71 ], and also can hurt the quality of life.

The average score of GDMQol was 55.51 ± 8.87%, and the highest and the lowest scores were related to “Support” and “Perceived constraints”, respectively. The results of the present study are similar to other studies that show that the average quality of life of pregnant women decreases after the diagnosis of GDM [ 29 , 46 , 61 , 72 ]. The quality of life of pregnant women with GDM is usually low, and about a quarter of pregnant women have a poor quality of life [ 29 ]. In the study of Simbar and colleagues, which was conducted on 200 women with GDM, the quality-of-life score was 46.83, and the highest and lowest scores belonged to the subscales of “support” and “concern about high-risk pregnancy”, respectively [ 46 ]. This low quality of life may be due to possible serious risks and adverse consequences for women with GDM and their babies. So women with GDM not only have to bear the physical, psychological, economic, and social problems of this disease but also, they are worried about the child’s health which seriously affects their quality of life [ 29 ].

The present study showed a significant negative relationship between GDMQoL and women’s age. This result is consistent with the results of some other studies showing significant negative effects of age on quality of life in diabetic patients [ 43 , 67 , 72 , 73 , 74 ]. In a population-based cross-sectional study of 13,358 pregnant women in China, Liu et al. showed that GDM and advanced maternal age were associated with decreased general health as one of the domains of quality of life [ 67 ]. As age increases, the adverse outcomes of pregnancy increase, which can negatively affect the quality of life [ 43 ].

There was also a significant negative relationship between BMI and GDMQoL. This relationship was found in other studies [ 47 , 75 , 76 , 77 ]. In a Path analysis by Ansarzadeh and colleagues, women’s age had an indirect effect on the quality of life through BMI, and a direct effect on the quality of life in GDM [ 43 ]. The relationship between obesity, GDM, and pregnancy outcomes can justify a low quality of life [ 78 ].

The finding indicated a significant negative correlation between the duration of marriage and GDMQoL scores. The negative effect of the duration of marriage on the quality of life was shown in some other studies [ 39 , 79 , 80 ]. According to a study, in the first 10 years of marriage, women experience a higher physical, mental, and environmental quality of life, and the duration of marriage can negatively affect various dimensions of quality of life [ 81 ]. Also, it seems increasing age can intensify the occurrence of adverse pregnancy complications and outcomes.

There was a significant positive association between the GDMQoL with the educational level of the women and also with the educational level of their spouses. Also, women’s education was a predictor of GDMQoL. A similar result was shown in some previous studies [ 46 , 47 , 82 ]. A study demonstrated that the level of education has a positive relationship with the mental health dimension of the quality of life in women with GDM [ 47 ]. Another study also demonstrated that pregnant women with higher education have higher scores for perceived mental health as well as perceived general health [ 83 ], while low education is a risk factor for impaired physical performance, which can affect the women’s quality of life [ 84 ].

We could not find a similar finding about the positive relationship between the spouse’s education and the quality-of-life score of women with GDM. It seems that the higher educational level of the spouse of women with GDM can be associated with a higher income level and more knowledge about care and support, which can have a positive effect on the women’s quality of life.

In our study, occupation was identified as an effective factor in the quality of life of women with GDM, so working women with GDM had a higher GDMQoL score than housewives. Also, occupation was a predictor for the GDMQoL score. Similarly, Kermansaravi and colleagues showed that working women scored higher than housewives in mental health and quality of life [ 85 ]. It is also demonstrated that education and occupation are the most important demographic factors in the physical dimension of the quality of life in pregnant women [ 86 ]. Educated and working mothers, due to their better socio-economic status, probably have a greater understanding of the importance of their health, and pay more attention to their appearance, weight control, and body mass index which can affect their health [ 87 , 88 ].

There was a significant relationship between the husband’s occupation with the GDMQoL score. So the score of the GDMQoL of women whose husbands were employees was higher than the group whose husbands were workers. Some studies showed that the husband’s job is an effective factor in the quality of life of pregnant mothers [ 86 , 89 , 90 ]. Higher job levels may lead to positive economic consequences, increased social support, a greater variety of leisure activities, access to health services, and more academic-, social-, and family successes, which can increase happiness and Women’s mental health [ 91 ].

Also, our findings revealed that proper income and economic class of the family have a positive impact on GDMQoL. The result is in line with other studies [ 47 , 77 , 90 , 92 ]. A higher quality of life was reported among women with GDM who had a high financial status. They had greater acceptance of illness which contributes to a higher quality of life and health status [ 59 ]. Although no study was found in women with GDM, the relationship between diabetes and the economic level of the family is numerous in the studies related to people with diabetes and they are indicative of the fact that the income level and economic conditions are among the variables related to the quality of life in diabetic women [ 77 , 93 , 94 , 95 , 96 ]. High-income families can afford medical services without financial barriers [ 96 ] and so can directly support the quality of life of diabetic patients. However, low-income families are concerned about spending on medical expenses which can cause stress and negatively affect the psychological dimension of the quality of life. Also, income is associated with other social factors such as occupation, education, and health and so affects the quality of life [ 97 ]. Finally, sociodemographic factors such as occupation, education, social class, and income are closely related and can certainly affect the quality of life.

Finally, it can be suggested that future studies about improving the quality of life in women with GDM concentrate on the different interventions to promote mental health, such as comparative studies on the effectiveness of different treatment methods.

Strengths and limitations

The use of GDMQoL-36 as a valid, reliable standard and a specific quality of life questionnaire for women with GDM instead of general questionnaires such as the World Health Organization quality of life questionnaire is the strength of this study.

A limitation of the study was that it was a cross-sectional study, which cannot dynamically describe the relationship between the quality of life and the duration of the disease in pregnant women with GDM. Also, the results of a cross-sectional study cannot judge precisely about cause-and-effect relationships of the variables.

Besides, the cross-sectional study makes it impossible to examine the longitudinal relationship between mental disorders such as depression, anxiety, and stress and GDMQoL. A path-analysis study by a social determinants approach and considering all associated factors are suggested for future studies.

GDMQoL is correlated with mental disorders including depression, anxiety, and stress. Among them, depression is a predictor of the GDMQoL. Fetal-maternal health promotion is crucial for improving the quality of life of women with GDM, and so attention to the mental health of women with GDM should be considered as a priority. Healthcare providers can use cognitive behavioral therapy, mindfulness-based stress and anxiety reduction therapy, and other psychosocial counseling for women with GDM. These treatments can help pregnant women to reduce mental pressure and increase self-confidence in treatment. These interventions can prevent depression and thereby improve mental health and improve the quality of life during pregnancy.

This study showed that some personal and social characteristics including age, BMI, length of marriage, education, occupation, income, and economic class are associated with GDMQoL. Therefore, to improve the quality of life, possible measures such as optimal weight control, financial support, and providing free care in the future plans in GDM prenatal care services.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

• Gestational diabetes mellitus

GDM-related quality of life questionnaire

Depression, anxiety and stress scale

American diabetes association

Large for gestational age

Body mass index

Diabetes Clients Quality of Life questionnaire

Scale content validity index

Scale content validity ratio

Analysis of variance

Statistical Package for the Social Sciences (version 23)

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SN designed the concept and study, managed the data, carried out data analysis, prepared the first and revised drafts of the manuscript, edited critically the manuscript. MS developed the project, designed the concept and study, managed the data, prepared the first and revised drafts of the manuscript, edited critically the manuscript. ZK developed the project, designed the concept and study, and managed the data. NK, MKK, and ZN carried out data collection. All authors have read and have approved the manuscript.

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Nazarpour, S., Simbar, M., Kiani, Z. et al. The relationship between quality of life and some mental problems in women with gestational diabetes mellitus (GDM): a cross-sectional study. BMC Psychiatry 24 , 511 (2024). https://doi.org/10.1186/s12888-024-05960-4

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DOI : https://doi.org/10.1186/s12888-024-05960-4

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GC indicates gestational carrier.

a Descriptive statistics of noncomparator studies are described in eTable 2, eTable 3, and eTable 4 in Supplement 1 .

The pooled odds ratio for unadjusted analysis for frozen embryo transfer (FET; A) and unadjusted analysis for single embryo transfer (SET; B) between GC pregnancies vs non-GC ART pregnancies. Heterogeneity among the studies in each analysis was defined as considerable heterogeneity in unadjusted random-effect analysis. Some values listed in the figure might be slightly different from the original values because of the calculation in RevMan version 5.4.1.

The pooled odds ratios are shown for unadjusted analysis for PTB (A), adjusted analysis for PTB (B), unadjusted analysis for LBW (C), and adjusted analysis for LBW (D) for GC pregnancies vs non-GC pregnancies. Heterogeneity among the studies in each analysis was defined as considerable heterogeneity in unadjusted random-effect analysis (A, C), no heterogeneity in adjusted fixed-effect analysis (B), and substantial heterogeneity in adjusted random effect analysis (D). Some values listed in the figure might be slightly different from the original values because of the calculation in RevMan version 5.4.1.

eAppendix 1. Searching Keywords

eAppendix 2. PICOS Criteria for Inclusion of Systematic Review

eAppendix 3. The Definition of Heterogeneity

eTable 1. Metadata of Comparator and Non-Comparator Studies

eTable 2. Maternal Characteristics of Gestational Carrier Pregnancies (Non-Comparator Studies)

eTable 3. Obstetric Outcomes Among Gestational Carrier Pregnancies (Non-Comparator Studies)

eTable 4. Obstetric Outcomes in Singleton Gestational Carrier Pregnancies (Non-Comparator Studies)

eTable 5. Risk of Bias Assessment for the Comparator Study

eReferences.

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Matsuzaki S , Masjedi AD , Matsuzaki S, et al. Obstetric Characteristics and Outcomes of Gestational Carrier Pregnancies : A Systematic Review and Meta-Analysis . JAMA Netw Open. 2024;7(7):e2422634. doi:10.1001/jamanetworkopen.2024.22634

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Obstetric Characteristics and Outcomes of Gestational Carrier Pregnancies : A Systematic Review and Meta-Analysis

• 1 Department of Gynecology, Osaka International Cancer Institute, Osaka, Japan
• 2 Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Suita, Japan
• 3 Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, Los Angeles
• 4 Department of Obstetrics and Gynecology, Osaka General Medical Center, Osaka, Japan
• 5 Keck School of Medicine, University of Southern California, Los Angeles
• 6 Department of Obstetrics and Gynecology, Division of Reproductive Endocrinology and Infertility, University of Southern California, Los Angeles
• 7 Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Southern California, Los Angeles
• 8 Norris Comprehensive Cancer Center, University of Southern California, Los Angeles

Question   What are the obstetric characteristics and outcomes of gestational carrier pregnancies?

Findings   This systematic review and meta-analysis evaluated 6 comparator studies involving 28 300 gestational carrier pregnancies and 1 270 662 non–gestational carrier pregnancies which showed that gestational carrier pregnancies had higher odds of hypertensive disorders than general pregnancies (ie, non–gestational carrier pregnancies with or without use of assisted reproductive technology) and comparable rates of preterm birth and low birth weight compared with non–gestational carrier pregnancies that used assisted reproductive technology. Severe maternal morbidity and maternal mortality were rare among gestational carriers.

Meaning   These findings suggest that the risk profile of gestational carrier pregnancies shares similarities to other assisted reproductive technology–conceived pregnancies.

Importance   Advancements in assisted reproductive technology (ART) have led to an increase in gestational carrier (GC) pregnancies. However, the perinatal outcomes of GC pregnancies remain understudied, necessitating a deeper understanding of their associated risks.

Objective   To assess maternal characteristics and obstetric outcomes associated with GC pregnancies.

Data Sources   A comprehensive systematic search of publications published before October 31, 2023, using PubMed, Web of Science, Scopus, and Cochrane Library databases was conducted.

Study Selection   Two authors selected studies examining obstetric characteristics and outcomes in GC pregnancies with 24 or more weeks’ gestation. Studies with insufficient outcome information, unavailable data on gestational surrogacies, and non-English language studies were excluded.

Data Extraction and Synthesis   Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, 2 investigators extracted and synthesized both quantitative and qualitative data. Both fixed-effect and random-effect analysis were used to pool data.

Main Outcomes and Measures   The primary outcomes were obstetric characteristics and outcomes, including hypertensive disorders, preterm birth, and low birth weight. Secondary outcomes included severe maternal morbidity and mortality associated with GC pregnancies.

Results   Six studies from 2011 to 2023 involving 28 300 GC pregnancies and 1 270 662 non-GC pregnancies were included. GCs accounted for 2.5% of in vitro fertilization cycles (59 502 of 2 374 154 cycles) and 3.8% of ART pregnancies (26 759 of 701 047 ART pregnancies). GC pregnancies were more likely to be conceived by frozen embryo transfer compared with non-GC ART pregnancies (odds ratio [OR], 2.84; 95% CI, 1.56-5.15), and rates of single embryo transfer were similar between the 2 groups (OR, 1.18; 95% CI, 0.94-1.48). GCs were rarely nulliparous (6 of 361 patients [1.7%]) and were more likely to have multifetal pregnancies compared with non-GC ART patients (OR, 1.18; 95% CI, 1.02-1.35). Comparator studies revealed lower odds of cesarean delivery (adjusted OR [aOR], 0.42; 95% CI, 0.27-0.65) and comparable rates of hypertensive disorders (aOR, 0.86; 95% CI, 0.45-1.64), preterm birth (aOR, 0.82; 95% CI, 0.68-1.00), and low birth weight (aOR, 0.79; 95% CI, 0.50-1.26) in GC pregnancies vs non-GC ART pregnancies. Comparatively, GC pregnancies had higher odds of hypertensive disorders (aOR, 1.44; 95% CI, 1.13-1.84) vs general (non-GC ART and non-ART) pregnancies with comparable cesarean delivery risk (aOR, 1.06; 95% CI, 0.90-1.25). Preterm birth and low birth weight data lacked a comparative group using multivariate analysis. Severe maternal morbidity and maternal mortality were rare among GCs.

Conclusions and Relevance   In this systematic review and meta-analysis, although GC pregnancies had slightly improved outcomes compared with non-GC ART pregnancies, they posed higher risks than general pregnancies. Contributing factors may include ART procedures and increased rates of multiple gestations which influence adverse perinatal outcomes in GC pregnancies.

Surrogacy involves a person carrying a fetus in utero and delivering a newborn for another person or people, termed the intended parents. 1 It can be classified as traditional or gestational surrogacy, which may also be referred to as full or host surrogacy. 1 Traditional surrogacy, now rare, uses the gametes (oocyte) of the surrogate, unlike gestational surrogacy, 2 in which an embryo from donated gametes or from the intended parents is implanted into a gestational carrier’s (GC) uterus. The increasing number of GCs is attributed to increased awareness of GC pregnancies, broader access to assisted reproductive technology (ART) services, and evolving surrogacy regulations. 3

In the US, gestational surrogacy is recommended if the intended parents face biological, medical, ethical, or psychosocial obstacles to pregnancy. 4 Some common indications for use of a GC include a biological inability to carry or gestate a pregnancy, a congenital absence of the uterus, or an acquired absence of the uterus secondary to cancer treatment, disease management, or obstetrical hemorrhage. 5 Additionally, in cases where an unknown endometrial factor hinders successful ART despite high-quality embryos, a GC may be considered in certain countries. Finally, many individuals with medical contraindications to pregnancy (eg, cervical insufficiency, autoimmune disease, or cardiac disease) may use a GC.

GCs may experience the potential risks associated with pregnancy and delivery, such as hypertensive disorders of pregnancy (HDP), gestational diabetes (GD), or cesarean delivery (CD) due to the high rate of multiple gestations, which is an iatrogenic complication of ART. ART for GCs may be associated with increased rate of placenta previa, placenta accreta spectrum, and placental abruption. 6 , 7 The objective of this study was to assess maternal characteristics and obstetric outcomes associated with GC pregnancies. Thus, we conducted a comprehensive systematic review and meta-analysis to assess these outcomes and gather information which is crucial for patient counseling, obtaining informed consent, and for identifying suitable candidates to be a GC. 4 We hypothesized that GC pregnancies would have worse pregnancy outcomes, including higher rates of HDP, preterm birth (PTB), and low birth weight (LBW) due to the increased rate of multiple pregnancies compared with general pregnancies.

This systematic review and meta-analysis was registered with the International Prospective Register of Systematic Reviews. 8 Because the current systematic review used publicly available and deidentified data, the Osaka International Cancer Institution institutional review board exempted the present study and the requirements of informed patient consent. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) reporting guideline. 9

Following prior methodologies, a systematic search of publications published before October 31, 2023, covered PubMed, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials using specific terms (eAppendix 1 in Supplement 1 ). 10 - 12 Titles, abstracts, and full texts were screened by 2 investigators (Shinya Matsuzaki and Satoko Matsuzaki). From this set, studies exploring the associations of GC pregnancy with relevant outcomes were extracted using keywords such as surrogate mothers (medical subject heading terms) or related keywords of surrogate mothers and pregnancy outcome (medical subject heading terms) or related keywords of pregnancy outcomes.

Study selection adhered to the patient population, intervention, comparator, outcome, and study type (PICOS) design (eAppendix 2 in Supplement 1 ). 9 The study inclusion criteria were (1) pregnancy outcomes in gestational surrogacy, (2) studies comparing obstetric outcomes between gestational surrogacy and nonsurrogacy, and (3) pregnancies with 24 or more weeks’ gestation. The exclusion criteria comprised (1) insufficient outcome information; (2) unavailable data on the number of gestational surrogacies; (3) non-English language studies; and (4) conference abstracts, editorials, case reports, case series, narrative reviews, systematic reviews, and meta-analyses.

Data were extracted by 2 investigators (Shinya Matsuzaki and Satoko Matsuzaki), who recorded the study year, location, first author’s name, number of cases, and the relevant outcomes. Pregnant individuals were classified into 3 groups: (1) GC pregnancies, (2) non-GC ART pregnancies, and (3) non-GC non-ART pregnancies. In this study, non-GC pregnancies were defined as non-GC ART and/or non-GC non-ART pregnancies, and general pregnancies included both non-GC ART and non-GC non-ART pregnancies.

The 2 primary outcomes were maternal characteristics and obstetric outcomes in GC pregnancies. Areas of interest included HDP, GD, fetal growth restriction, PTB, LBW, intrauterine fetal death, placenta previa, and placental abruption. In the sensitivity analysis, the characteristics of ART treatment of GC pregnancies were explored.

Secondary outcomes included severe maternal morbidity (SMM) and delivery outcomes, such as the rate of CD and postpartum hemorrhage. SMM was based on definitions by the Centers for Disease Control and Prevention (including eclampsia, blood transfusion, and hysterectomy), 13 and modified to include maternal death; intensive care unit admission; and hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome. A composite of SMMs determined in each study was used for analysis. Risk of bias assessment employed the Risk of Bias in Nonrandomized Studies of Interventions Tool (ROBINS-I). 14 - 16

Maternal outcome risks were estimated from the eligible studies in experimental and control groups using 95% CIs of reported values to derive odds ratios (ORs). Studies that did not provide raw data were excluded; the majority of studies presented ORs. Study heterogeneity was assessed using I 2 percentages and a fixed- or random-effect analysis was performed as shown in eAppendix 3 in Supplement 1 . Data from continuous and bivariate outcomes were entered for consistency, favoring active interventions due to negative effect sizes or relative risks less than 1. Any adjusted results were based on adjustments that were defined by the original studies to account for confounding variables.

Baseline demographic differences between groups were assessed using the χ 2 or Fisher exact test. Meta-analysis and visualizations were performed using RevMan software version 5.4.1 (Cochrane Collaboration). Statistical analyses were also conducted with SPSS version 28.0 (IBM). A 2-sided P  < .05 was considered statistically significant.

Of 4231 studies reviewed, 22 studies reported the obstetric outcomes of GC pregnancies ( Figure 1 and eTable 1 in Supplement 1 ). 3 , 17 - 37 Two studies with overlapping data were identified, 22 , 23 and the older study was excluded from the descriptive analysis. 23 Fifteen noncomparator studies were excluded from the main comparator analysis (eTables 2-4 in Supplement 1 ). 18 - 21 , 24 , 26 , 27 , 29 , 31 - 37 As a result, 6 studies involving 28 300 GC pregnancies and 1 270 662 non-GC pregnancies underwent further descriptive analysis ( Figure 1 ). 3 , 17 , 22 , 25 , 28 , 30

All 6 studies 3 , 17 , 22 , 25 , 28 , 30 were retrospective studies published between 2011 and 2023 (no randomized clinical studies). The majority of the studies (5 studies [83.3%]) originated from the US 3 , 17 , 22 , 25 , 30 and 1 study (16.7%) was from the UK. 28 Among the 6 studies, 1 compared obstetric outcomes between GC pregnancies and general pregnancies, 22 and all compared the outcomes between GC pregnancies and non-GC ART pregnancies ( Table 1 ). 3 , 17 , 22 , 25 , 28 , 30

In the 6 eligible studies, age at ART treatment and ART type (frozen or fresh embryo transfer) were reported in 4 studies 3 , 17 , 25 , 28 and rate of single embryo transfer was reported in 3 studies 3 , 17 , 25 ; prior live birth was not reported in any study ( Table 1 ). The method of endometrial preparation was unavailable in all studies. Maternal age and nulliparity were specified in 1 study, 22 and multiple pregnancy rates were specified in 5 studies 3 , 17 , 22 , 25 , 28 ( Table 2 ).

Among the 6 identified comparator studies (GC pregnancies vs non-GC pregnancies), risk of bias assessments were conducted. There was moderate bias (moderate quality) in 4 studies 3 , 17 , 22 , 28 and severe bias (low quality) in 2 studies 25 , 30 (eTable 5 in Supplement 1 ).

The following relevant outcomes were assessed in the 6 comparator studies: HDP (1 study), 22 GD (0 studies), PTB (4 studies), 3 , 17 , 25 , 28 LBW (4 studies), 3 , 25 , 28 , 30 CD (1 study), 22 maternal mortality (1 study), 22 and SMM (1 study). 22 Adjusted ORs (aORs) of obstetric outcomes in multivariate analyses were detailed for HDP (1 study), 22 PTB (2 studies), 25 , 28 LBW (2 studies), 25 , 28 and CD (1 study). 22

The cumulative rate of GC in vitro fertilization cycles was 2.5% (59 502 of 2 374 154 cycles) among ART cycles. GC pregnancies were more likely to be conceived by frozen embryo transfer than non-GC ART pregnancies (OR, 2.84; 95% CI, 1.56-5.15), whereas the use of single embryo transfer was similar between the 2 groups (OR, 1.18; 95% CI, 0.94-1.48) ( Figure 2 ).

Among the 6 comparator studies, 1 clarified the number of GC pregnancies among general pregnancies. 22 Four studies clarified the number of GC pregnancies among ART pregnancies, 3 , 17 , 25 , 28 and 2 studies were excluded because the total number of ART pregnancies was not clarified ( Table 1 ). 22 , 30 Based on this data, GC pregnancies comprised approximately 3.8% of ART pregnancies (26 759 of 701 047 ART pregnancies) and 0.1% of all pregnancies (361 of 509 376 pregnancies).

Of the 6 comparator studies, 1 compared maternal age between GC pregnancies and non-GC ART pregnancies ( Table 2 ). 22 The median (IQR) maternal age was lower in GC pregnancies (31 [28-34] years) than in non-GC ART pregnancies (38 [33-43] years]) ( P  < .001). There was a lower prevalence of nulliparity among GC pregnancies (6 of 361 [1.7%]) than among general pregnancies (166 441 of 509 015 [32.7%]) ( P  < .001); there was also a lower prevalence of nulliparity among GC pregnancies than or non-GC ART pregnancies (350 of 563 [62.2%]) ( P  < .001). In the noncomparator studies, 18 - 21 , 24 , 26 , 27 , 29 , 31 - 37 the cumulative mean (range) maternal age of GCs was 34.2 (32.7-38.8) years and the rate of nulliparity was 1.0% (12 of 1222 patients; range 0.1%-3.0%).

Five studies 3 , 17 , 22 , 25 , 28 included information on multiple gestation (ranging from 3197 of 21 649 pregnancies [14.8%] to 1453 of 3857 pregnancies [37.7%]). One study 22 reported a significantly higher rate of multiple gestation in GC pregnancies as compared with general pregnancies (OR, 15.27; 95% CI, 11.86-19.66). When compared with non-GC ART pregnancies, patients with GC pregnancies were more likely to have multiple gestation (OR, 1.18; 95% CI, 1.02-1.35). 3 , 17 , 22 , 25 , 28 In the noncomparator studies, 18 - 21 , 24 , 26 , 27 , 29 , 31 - 37 the cumulative rate of multiple gestation (excluding singleton-restricted studies) reached 21.6% (443 of 2055 patients; range 0.0%-34.7%).

One comparator study 22 compared HDP risks between GC pregnancies and general pregnancies and between GC pregnancies and non-GC ART pregnancies ( Table 2 ). This study found more HDP in GC pregnancies than in general pregnancies (aOR, 1.44; 95% CI, 1.13-1.84) and similar HDP risk between GC pregnancies and non-GC ART pregnancies (aOR, 0.86, 95% CI, 0.45-1.64). 22 Singleton deliveries–specific analysis was unavailable for HDP.

PTB was assessed in 4 of 6 comparator studies. 3 , 17 , 25 , 28 In the pooled analysis, PTB risks were comparable between GC pregnancies and non-GC ART pregnancies, evident in both unadjusted random-effects (OR, 0.93; 95% CI, 0.74-1.17; I 2  = 95%; χ 2 3  = 65.84; P  < .001) and adjusted fixed-effects analyses (available only for singleton pregnancies restricted data in 2 studies 25 , 28 ) (aOR, 0.82; 95% CI, 0.68-1.00; I 2  = 0%; χ 2 1  = 0.68; P  = .41) ( Figure 3 ).

Four comparator studies assessed LBW in GC pregnancies. 3 , 25 , 28 , 30 The pooled analysis indicated comparable LBW risk between GC pregnancies and non-GC ART pregnancies in both unadjusted random-effects (OR, 0.80; 95% CI, 0.57-1.13; I 2  = 89%; χ 2 3  = 27.76; P  < .001) 3 , 25 , 28 , 30 and adjusted random-effects analyses (available only for singleton pregnancies restricted data in 2 studies 25 , 28 ), but the adjusted analysis was not statistically significant (aOR, 0.79; 95% CI, 0.50-1.26; I 2  = 72%; χ 2 1  = 3.52; P  = .06) ( Figure 3 ).

A nationwide study in the US investigated the association of GC pregnancies with CD risk. 22 In this unadjusted analysis, GC pregnancies had similar CD risk compared with general pregnancies (OR, 1.21; 95% CI, 0.96-1.54) but a lower CD risk compared with non-GC ART pregnancies (OR, 0.22; 95% CI, 0.17-0.30) ( Table 2 ). These findings were consistent in multivariate analysis, indicating comparable CD rates between GC pregnancies and general pregnancies (aOR, 1.06; 95% CI, 0.90-1.25) and lower CD risks in GC pregnancies compared with non-GC ART pregnancies (aOR, 0.42; 95% CI, 0.27-0.65). Singleton-specific analysis was unavailable for CD.

SMM was determined in 1 comparator study 22 that assessed the composite evaluation of intensive care unit admission, eclampsia, HELLP, transfusion, and hysterectomy. The composite risk of SMM was similar between GC pregnancies and general pregnancies (aOR, 1.03; 95% CI, 0.51-2.07), whereas the composite risk was lower in GC pregnancies compared with non-GC ART pregnancies (aOR, 0.17; 95% CI, 0.04-0.81). Maternal mortality was assessed in 1 comparator study, 22 which included 361 GC pregnancies with no maternal deaths, whereas 256 of 509 015 cases of maternal death (0.1%) were seen in general pregnancies (OR, 2.74; 95% CI, 0.17-44.06) ( Table 2 ).

The results from this systematic review and meta-analysis demonstrated 4 principal findings. First, GC pregnancies represented 3.8% of ART pregnancies and 0.1% of all pregnancies. Second, although there was insufficient evidence on obstetric outcomes of GC pregnancies, especially regarding SMM, these pregnancies often involve multiparous patients and a high rate of multiple gestations. Third, obstetric outcomes of GC pregnancies, excluding CD and SMM, were similar to those seen in ART pregnancies but could result in worse outcomes compared with unassisted pregnancies. Fourth, GC pregnancies may have higher HDP risks than non-GC pregnancies. While some findings aligned with existing knowledge, the scarcity of comparator studies in prospective settings underscores the need for further investigation.

Pregnancies in multiparous patients with a history of successful, uncomplicated term pregnancies are typically lower risk. 38 However, studies consistently found associations of ART pregnancies with higher risk of adverse obstetric outcomes and multiple gestation compared with non-ART conceptions. 39 - 41 Notably, the cause of infertility further elevates risk of adverse obstetric outcomes during ART pregnancies, particularly with multiple gestation, leading to increased chances of PTB, HDP, and LBW. 42 - 44 Thus, ART and multiple gestation may be the main factors associated with increased risk for GC pregnancies, whereas multiparous patients without infertility and a history of uncomplicated pregnancies tend to have good prognosis.

A retrospective study 27 showed higher odds of twin pregnancies, PTB, GD, placental previa, and CD in GC pregnancies (103 patients) vs the GC’s own prior pregnancies (294 patients). Another 2020 study in the US with a limited sample size found similar obstetric outcomes (PTB, GD, postpartum hemorrhage, fetal growth restriction, placental abruption, and abnormal placentation) when comparing a GC singleton pregnancy (78 patients) with their own prior singleton pregnancies (71 patients). 37

A retrospective study 21 that compared the obstetric outcomes between GCs with singleton gestation (284 pregnancies) and multiple gestation (77 pregnancies) showed that GC pregnancies with multiple gestation had increased odds of PTB compared with GC pregnancies with singleton gestation (aOR, 29.3; 95% CI, 11.0-78.0) and CD (aOR, 5.6; 95% CI, 3.1-10.2). The poorer prognosis of GC pregnancies may primarily stem from higher rates of multiple gestation. Therefore, efforts to reduce multiple gestation, such as elective single embryo transfer, are crucial to improving the obstetric outcomes of GC pregnancies.

In the present study, PTB and LBW risks were comparable between GC pregnancies and non-GC ART pregnancies, but no studies included non-GC non-ART controls. One study 22 found lower CD risks but similar HDP risks in GC pregnancies than in non-GC ART pregnancies. The increased HDP following oocyte donation is theorized to be caused by an immunological maladaptation to the foreign antigens from the fetus. 28 , 45 Gestational surrogacy involves carrying a pregnancy that is the result of either the intended parent’s or parents’ gametes, or donor gametes. 28 Theoretically, immune reactions to foreign antigens in gestational surrogacy may be comparable with responses observed in people after receiving oocyte donations, which increases the risk of HDP compared with autologous ART. 28 , 45

Another potential factor that could increase the risk of HDP in GC pregnancies is the widespread use of frozen embryo transfer. In this study, GCs were more likely to conceive with frozen embryo transfer than non-GCs. A meta-analysis of 3 randomized trials 46 involving 1193 pregnancies after frozen embryo transfer and 1205 after fresh embryo transfer showed an increased risk of HDP after frozen embryo transfer compared with fresh embryo transfer. A population-based study in Norway 47 reported a comparable HDP risk between fresh embryo transfer and natural conception (aOR, 1.02; 95% CI, 0.98-1.07), whereas frozen embryo transfer was associated with an increased HDP risk compared with natural conception (aOR, 1.74; 95% CI, 1.61-1.89). Although the type of endometrial preparation was unavailable in this study, these data are suggestive in that frozen embryo transfer use in GCs could potentially increase the risk of HDP.

Given limited information regarding patient characteristics in GCs, descriptive statistics of noncomparator studies are summarized in eTables 2 to 4 in Supplement 1 . 18 - 21 , 24 , 26 , 27 , 29 , 31 - 37 In the noncomparator studies, the cumulative mean maternal age of GCs, the cumulative rate of multifetal gestation, and the rate of nulliparity results may be consistent with comparator studies and enhance the robustness of the results of the meta-analysis; however, it could not be fully determined because the reported outcomes were different between the comparator and noncomparator studies.

The American Society for Reproductive Medicine (ASRM) committee recommends selecting GCs who are aged 21 to 45 years, with at least 1 previous term delivery without complications, less than 5 previous deliveries, less than 3 previous CDs, and a stable family environment. 4 In adherence to these recommendations, GC pregnancies will have the following characteristics: (1) fertile multiparous patients with previous uncomplicated deliveries, (2) younger maternal age, (3) normal psychological evaluation, and (4) normal medical evaluation. Although GCs have these characteristics, GC pregnancies are a result of ART, which has a known risk of multiple gestation pregnancy. Given the notable risks described throughout the data, gestational surrogates should undergo rigorous screening and testing to determine appropriate candidates.

SMM was examined in only 1 comparator study 22 that was underpowered due to the limited number of included GC pregnancies (361 patients). Thus, the risk of SMM in GC pregnancies is still unknown. More extensive studies are needed to examine the association of GC pregnancies with SMM. Notably, 1 study 23 evaluated outcomes in GC pregnancies in patients who did not match the ASRM safety guidelines. The outcomes showed a correlation with severe obstetric and neonatal complications. Although SMM was rare in these GC pregnancies that did not abide by ASRM guidelines, these pregnancies did have higher rates of CD, neonatal morbidity, and PTB. Thus, careful GC candidate selection may substantially improve obstetric outcomes.

This study had several limitations. First, there was inherent bias from the inclusion of retrospective studies as well as confounding variables. Second, no included studies comprehensively analyzed patient obstetric history to investigate the association of gestational surrogacy with obstetric outcomes. Consequently, a causal relationship between gestational surrogacy and maternal outcomes could not be established. Third, publication bias remains a substantial concern, potentially skewing findings toward positive associations of maternal outcomes with gestational surrogacy.

Fourth, there are limited comparative studies examining maternal outcomes in gestational surrogacy, requiring more comprehensive investigations. Moreover, eligible studies of the current systematic review were missing key outcome variables, thus limiting the ability to perform meaningful meta-analyses and limiting the conclusiveness and impact of the study. This weakness was due to the limited available obstetric outcomes of the data sources used in the eligible studies. Given the challenges associated with conducting a randomized clinical trial, a prospective study may be appropriate.

Fifth, data on previous pregnancies were absent, but a history of PTB, HDP, or CD is associated with an increased rate of subsequent pregnancy complications. Finally, psychological and physical evaluations for gestational surrogates could decrease obstetric complications, potentially introducing selection bias. Acknowledging this limitation is crucial when interpreting the results of the current study.

Sixth, although multiple gestations are associated with adverse outcomes, the outcomes controlling for gestation in the analysis were unavailable because of the lack of data in the eligible studies. Elective single embryo transfer is recommended to decrease multiple gestation in ART pregnancies, especially in GC pregnancies. In alignment with the nationwide push promoting single embryo transfer, it is likely that some of these outcomes have improved over time. Nevertheless, these data were not available for the current systematic review and further research considering these factors is warranted to improve the obstetric outcomes of GC pregnancies.

In this systematic review and meta-analysis of characteristics and maternal outcomes of GC pregnancies, we found comparable obstetric outcomes between GC pregnancies and non-GC ART pregnancies, but a lack of comparisons with non-GC non-ART pregnancies persists. There is a need for further research to comprehensively understand obstetric outcomes in GC pregnancies and better understand the associated risk profile.

Accepted for Publication: May 17, 2024.

Published: July 23, 2024. doi:10.1001/jamanetworkopen.2024.22634

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Matsuzaki S et al. JAMA Network Open .

Corresponding Authors: Koji Matsuo, MD, PhD, Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Southern California, 2020 Zonal Ave, IRD520, Los Angeles, CA 90033 ( [email protected] ); Shinya Matsuzaki, MD, PhD, Department of Gynecology, Osaka International Cancer Institute, 3-1-69 Otemae, Chuo-ku, Osaka, Osaka 540-0008, Japan ( [email protected] ).

Author Contributions: Drs Shinya Matsuzaki and Satoko Matsuzaki had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Drs Shinya Matsuzaki, Masjedi, and Satoko Matsuzaki contributed equally to the work.

Concept and design: Shinya Matsuzaki, Masjedi, Mandelbaum, Paulson, Matsuo.

Acquisition, analysis, or interpretation of data: Shinya Matsuzaki, Masjedi, Satoko Matsuzaki, Anderson, Erickson, Mandelbaum, Ouzounian, Matsuo.

Drafting of the manuscript: Shinya Matsuzaki, Masjedi, Satoko Matsuzaki, Mandelbaum, Matsuo.

Critical review of the manuscript for important intellectual content: Masjedi, Anderson, Erickson, Mandelbaum, Ouzounian, Paulson, Matsuo.

Statistical analysis: Shinya Matsuzaki, Satoko Matsuzaki, Erickson, Mandelbaum, Matsuo.

Obtained funding: Matsuo.

Administrative, technical, or material support: Mandelbaum, Ouzounian, Matsuo.

Supervision: Mandelbaum, Paulson, Matsuo.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was supported by the Ensign Endowment for Gynecologic Cancer Research (to Dr Matsuo).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2 .

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• v.34; 1988 Sep

Gestational Diabetes: A Review

Gestational diabetes mellitus is a relatively common complication of pregnancy. The incidence varies from 1.6% to 13%, depending on the criteria used for evaluating glucose tolerance in studies where universal screening was employed. The glucose-challenge screening test produces many false-positive results; the patients thus identified are then subjected to further unpleasant oral glucose-tolerance tests to make the diagnosis. The diagnosis labels many pregnant patients as “high risk” and exposes them to a cascade of interventions. The author examines the basis in the literature for universal screening practices. The recommendations of the Second International Workshop-Conference on Gestational Diabetes Mellitus are presented. The author discusses risks and benefits of alternative screening approaches, diagnosis, control, and reviews the current literature.

Full text is available as a scanned copy of the original print version. Get a printable copy (PDF file) of the complete article (1.4M), or click on a page image below to browse page by page. Links to PubMed are also available for Selected References .

Selected References

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