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Abstract
Introduction Women with gestational diabetes mellitus (GDM) are more likely to give birth to large for gestational age (LGA) infants, due to abnormalities in glucose metabolism during pregnancy. Although previous studies have explored the risk factors for LGA delivery in GDM women, the results are quite different and still lack of unified understanding.
Objective To explore the elements linked to LGA delivery in GDM women, and thus provide a reference for medical staff to formulate relevant clinical interventions.
Methods and analysis Systematic search of seven electronic databases (PubMed, Scopus, Cochrane Library, Web of Science, EMBASE, OVID and CINAHL) will be undertaken between the inception of the database to 1 August 2024. Quantitative studies published in English and focused on the risk factors for LGA delivery in GDM women will be included. Two researchers will independently screen the literature and any disagreements will be resolved by a third-party researcher. Joanna Briggs’s Institutional Critical Appraisal Tools will be used for the quality assessment of included studies. RevMan V.5.4 software will be used for data processing and summarising. To ensure the reliability and stability of the results, Q test and I2 test will be used to identify the heterogeneity between studies, while subgroup analysis and sensitivity analysis will be performed based on study quality.
Ethics and dissemination This systematic review and meta-analysis will be based on published literature, and the findings will be published in a peer-reviewed journal and presented at major conferences focused on clinical nursing.
PROSPERO registration number CRD42024559013.
- Diabetes in pregnancy
- Fetal medicine
- Risk Factors
- Meta-Analysis
- Protocols & guidelines
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STRENGTHS AND LIMITATIONS OF THIS STUDY
This will be the first systematic review and meta-analysis specifically designed to explore risk factors of large for gestational age (LGA) delivery among women with gestational diabetes mellitus (GDM) and its prevalence.
Both GDM and LGA will be diagnosed using unified standards to ensure consistency and comparability among the studies included.
To explore and clarify the sources of heterogeneity, subgroup analysis and sensitivity analysis will be conducted based on the quality of the research.
The study may be limited by the small sample of certain studies, resulting in inaccurate pooled effect estimates.
The study may be constrained by the differences in research regions and publication years, potentially resulting in some heterogeneity among the studies.
Introduction
Gestational diabetes mellitus (GDM), a transient abnormality in glucose metabolism that occurs during the second and third trimesters of pregnancy, has become an increasingly critical health concern affecting both mothers and infants.1 2 With the general increase in the age of women giving birth, coupled with changes in dietary structure and factors such as pre-pregnancy obesity, the incidence rate of GDM has risen significantly to 14.8%.3 4 It is estimated that globally, 1.84 million (13.9%) newborns are affected by maternal hyperglycaemia during pregnancy. Additionally, compared with pregnant women with normal glucose tolerance, the risk of adverse maternal and infant outcomes in GDM women will be increased, such as abnormal newborn weight, birth injury and long-term metabolic disorders in adolescence.5
Since most GDM women show metabolic abnormalities in glucose levels, which are significantly positively correlated with newborn birth weight.6–8 Studies have shown that large for gestational age (LGA) is one of the most common adverse pregnancy outcomes in GDM women.9 10 LGA is defined as birth weight above the 90th percentile of the average weight for the same gestational age, sex and race.11 The average incidence of LGA in pregnancies with GDM is 16.3% (ranges, 3.5%–37.7%), a 1.66-fold increase compared with those without GDM.12 LGA can cause plenty of adverse effects on pregnant women and newborns.13 Studies have shown that LGA can increase the risk of prolonged labour, caesarean section, birth canal laceration and postpartum haemorrhage, as well as the risk of intrauterine distress, shoulder dystocia and brachial plexus loss of the fetus.14–16 Meanwhile, the LGA of full-term singletons has a greater risk of macrosomia and faces a significantly heightened risk of severe perinatal complications.17 18 On the other hand, excessive weight gain in the womb can also lead to an increased risk of overweight or obesity, diabetes, metabolic syndrome and other problems in the future of the newborn.19 20
Given the profound impact of LGA on both maternal and neonatal health, identifying the risk factors is of paramount importance. Previous studies have reported the risk factors for LGA delivery in GDM women, including inappropriate gestational weight gain, plasma glucose levels, serum ghrelin levels, plasma lipid levels and maternal body mass index.21–27 These factors are intricately linked to the complex interplay of metabolic disorders and pregnancy outcomes. This interconnection underscores the necessity for a comprehensive understanding and targeted interventions to mitigate the risks associated with GDM and LGA deliveries. However, despite the identification of a range of risk factors, there may be disagreements among different studies in assessing risk factors such as gestational weight gain, glycaemic control and lipid levels.20 21 26 Furthermore, the identification and analysis of risk factors are also influenced by differences in study types, regions and the size and characteristics of the sample.11 13 22 These have not only sparked some debates but also indicated an urgent need for systematic review and meta-analysis. Systematic review and meta-analysis enable the aggregation of extant research data, facilitating the elucidation of the relative significance of risk factors and laying a robust scientific groundwork for the formulation of efficacious preventative strategies.
With the increasing prevalence of high birth weight due to changes in lifestyle and diet, the concern surrounding this issue is becoming more pronounced.28 This growing concern further emphasises the importance of a systematic approach to understand the risks associated with GDM and LGA deliveries. Given the current gap in systematic reviews on risk factors, this study aims to construct a systematic review and meta-analysis protocol to explore and identify all risk factors for LGA delivery in GDM women. Through this study, we aspire to provide early warning for clinical staff and pregnant women with GDM, guiding the formulation of targeted preventive interventions at the earliest clinical stage.
Objective
The objective of this systematic review is to identify risk factors associated with LGA delivery among GDM women. This review will be guided by the research questions: What are the risk factors for GDM women who give birth to LGA?
Methods and analysis
Study registration
This systematic review and meta-analysis protocol will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA; online supplemental file 1). And this protocol has been registered with PROSPERO under registration number (No. CRD42024559013). This study will be started from September 2024 to December 2024.
Supplemental material
Types of studies
All quantitative studies (experimental studies, quasi-experimental studies and observational studies) that focused on the elements affecting LGA delivery in GDM women are acceptable for this study and there is no time limitation for publication. On the basis that the full content of the literature is accessible, we will comprehensively examine and analyse qualified research from any region of the globe, and the subjects are human beings. The initial search period for the literature will run from the inception of the database to 1 August 2024, with additional searches to account for recent developments as they approach completion. Within this time frame, the literature search procedure shall be ongoing.
Search strategy
Seven databases (PubMed, Scopus, Cochrane Library, Web of Science, EMBASE, OVID and CINAHL) will be comprehensively searched. In order to improve the accuracy of the retrieval content and avoid omissions as much as possible, the author team has formulated a strict retrieval strategy with a combination of Medical Subject Headings (MeSH), keyword terms and filters (figure 1) using the VOSviewer software tool to visualise bibliometric networks. The search strategy for different databases is presented in table 1 summarising the key search terms for population and outcome. And the detailed search strategies for all databases are described in the online supplemental file 2.
Supplemental material
Key terms for developing search strategy
Cluster analysis of keywords from PubMed database.
The initial literature search will be conducted in the PubMed database, a step designed to evaluating the effectiveness of keywords, search strategies and search scope in identifying relevant literature. Afterwards, adjustments will be made based on the result of this step and full searches will be conducted in the remaining electronic databases. Besides, for reference lists of relevant literature, especially systematic reviews related to the topic of this study, additional searches will also be made in the electronic databases, making the included studies more comprehensive. All searches are conducted under the supervision of an academic librarian.
Condition/domain being studied
The conditions being studied included women diagnosed with GDM who delivered LGA. Due to the differences in GDM diagnostic criteria across various regions and studies, we will convert the GDM diagnosis data from all studies to the International Association of the Diabetes and Pregnancy Study Groups (IADPSG) standard. If the thresholds used in the original studies are different from the IADPSG, conversions will be performed using the correlation between blood glucose levels and the risk of GDM. For studies that cannot be converted to the IADPSG standard, a sensitivity analysis will be conducted to assess the impact of the varying diagnostic criteria on the pooled effect size.
Subjects
The most focused subjects of this systematic review and meta-analysis will be GDM women from all ethnicities and all over the country. According to IADPSG, GDM is diagnosed using the 75 g oral glucose tolerance test (OGTT) during 24 to 28 weeks’ gestation. Under the standard of IADPSG, GDM can be defined as fasting blood level (FBG) ≥ 5.1 mmol/L, a 1-hour glucose level≥10.0 mmol/L, or a 2-hour glucose level ≥8.5 mmol/L.29 Besides, LGA is diagnosed as a birth weight of newborn exceeding the 90th percentile, for the same gestational age, sex and race.11
Exposure
The exposure of this systematic review and meta-analysis will be all demographic, physiological (routine/medical), pathological and social risk factors associated with LGA delivery in GDM women.
Outcomes
The outcome of this systematic review and meta-analysis will be to identify the risk factors related to LGA delivery in GDM women and its prevalence.
Inclusion criteria
The inclusion criteria for this systematic review and meta-analysis will be as follows:
Participants: GDM women who meet the diagnostic criteria for IADPSG.
Study types: quantitative studies.
Exposure factors: risk factors that may lead to the delivery of LGA in women with GDM.
Outcomes: newborns who meet the diagnostic criteria for LGA.
Study data: relative risk (RR), HR or OR and 95%CI.
Exclusion criteria
The exclusion criteria will be as follows:
Studies that cannot provide complete data.
Studies that is inaccessible.
Literature reviews, meta-analysis, conference abstracts, comments, letters to the editor and research protocol.
Studies not published in the English language.
After completing the initial search according to the search strategy, all search records will be exported to the Endnote 21 reference management software, and then the ‘Find Duplicates’ function of this software will be used to remove duplicate studies. Two authors will independently read the titles and abstracts of the remaining literature and remove those that do not meet the inclusion. After this step, the same two authors will obtain the full text of the pre-screened literature and critically evaluate its content based on the exclusion criteria. All differences between the two authors will be discussed first. When unification is not possible, it will be resolved by the third author. PRISMA flow diagrams (figure 2) depict the literature selection process during the study period.
Preferred Reporting Items for Systematic Reviews and Meta-Analysis flow diagram of the identification, screening and eligibility of included articles.
Data extraction and management
Data extraction will be conducted using a Microsoft Excel spreadsheet (Microsoft, Washington, USA). In the process of data extraction, two authors will independently extract relevant information from the literatures that meet the standards according to the following contents: (1) title of the study; (2) author of the study; (3) time of publication; (4) country of the study; (5) type of study; (6) sample size; (7) incidence rate; (8) diagnostic criteria for GDM and LGA; (9) risk factor; (10) RR/HR and 95% CIs or ORs and 95% CIs for the risk factors (studies using multivariate regression analysis methods); (11) quality evaluation score; (12) whether to adjust for confounding factors. When the content presentation is unclear, the researcher will proactively contact the corresponding author to obtain information. If the author cannot provide the required data, the literature will be excluded. After two researchers have independently completed their tasks, they will consolidate and review each other’s work. Any disagreements will be resolved by a third-party arbitrator.
Risk of bias and quality assessment
Two authors will independently evaluate the quality of the included studies and risk of bias using the Joanna Briggs Institute (JBI) Critical Appraisal Tools for experimental, quasi-experimental and observational studies within JBI SUMARI.30 As with the data extraction process, disagreements between two authors will be recorded and resolved by consensus or with the help of the third author. The results of the included studies and their corresponding key assessment criteria (yes, no or unclear) will be reported in a table accompanied by a narrative.
Strategy for data synthesis
In this study, RevMan V.5.4 software will be used for statistical analysis of risk factors of the included literature. In studies where confounding factors have been appropriately controlled, the effect index represents the adjusted impact, considering the most influential confounders. On the other hand, if the studies are incorporated without the correction of confounding factors, the raw effect will be extracted and considered as the research data. When the effect index is the HR value, it will be directly comparable to the RR value for subsequent analysis; when the effect index is the OR value, no additional processing will be required. If a study stratifies a single risk factor and reports only the effect index for each stratum, the stratified data will be pooled into an overall effect index using a fixed-effects model (FEM).
Subsequently, a comprehensive analysis will be conducted, integrating these findings with the research data from other literature. Q test (test level α=0.1) and I2 test will be combined to quantify the heterogeneity between studies. If there is no heterogeneity (I2≤50%, p>0.10), the FEM will be used for meta-analysis; if there is heterogeneity (I2>50%, p≤0.1), a random-effects model will be used for meta-analysis. And the source of heterogeneity will be investigated. When quantitative synthesis is not appropriate, narrative synthesis will be performed in this study.
Subgroup analysis and sensitivity analysis
On condition that data is available, subgroup analysis will be performed to assess heterogeneity based on patient age, study type, survey region and literature quality scores. Furthermore, sensitivity analysis will be performed on all risk factors, using gradually excluding studies, to ascertain the stability and reliability of the outcomes.
Assessment of reporting biases
The funnel plot will be drawn for individual risk factors with more than 10 studies and combined with Egger’s test and Begg’s test to evaluate the publication bias. Egger’s test will be used to assess the asymmetry of the funnel plot. Additionally, Begg’s test will be used to assess bias by contrasting the observed effect sizes from the studies with those anticipated in the absence of publication bias.31 For studies with identified publication bias, the cut-off method will be used to estimate the number of missing studies. On condition that p<0.05, it indicates that the asymmetry in the funnel plot is significant and may indicate the presence of publication bias.
Patient and public involvement
None.
Ethics and dissemination
This systematic review and meta-analysis will be based on published literature, and thus there is no need for ethical approval. And the findings will either be published in a peer-reviewed journal.
Ethics statements
Patient consent for publication
References
Footnotes
YL and WD contributed equally.
Contributors YL and WD designed and developed the research question. YL and FS developed the search strategy. YL registered the protocol and wrote the first draft of the manuscript. YS and YL are the guarantors. HL and JS developed the risk of bias assessment strategy. FS, HY and JS helped to revise the manuscript. All authors read and approved the final manuscript.
Funding This work was supported by the National Natural Science Foundation of China (No. 32070189).
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.