Conflicting evidence for continuous glucose monitoring in gestational diabetes: current challenges and the imperative for individual participant data meta-analysis

Abstract

Background

The incidence of gestational diabetes mellitus (GDM) is rising along with rates of obesity and type 2 diabetes. GDM is associated with an increased risk of various short- and long-term complications for both mother and child, including type 2 diabetes. Managing GDM requires a multidisciplinary approach that includes dietary guidance, promoting regular physical activity, and performing self-monitoring of blood glucose (SMBG). Continuous glucose monitoring (CGM) has transformed pregnancy care in women with type 1 diabetes and is increasingly being proposed as a strategy to improve glycaemic management in GDM. By capturing postprandial glucose excursions, glycaemic variability, and time in range, CGM provides a more detailed picture of glycaemia and has raised expectations of improved metabolic control and pregnancy outcomes.

Main body

Despite these theoretical advantages, evidence for CGM in GDM remains conflicting. More recently, several large randomized controlled trials (RCT’s) show conflicting data: some studies show modest improvements in glycaemic measures, whereas others report no clear benefit for key maternal or neonatal outcomes, leaving the clinical value and cost-effectiveness of CGM unclear. A major limitation is the absence of validated CGM-based glycaemic targets specific to GDM, and current guidelines offer little direction beyond conventional SMBG guidelines. Future research should prioritize adequately powered large RCT’s representing a broad population, and, importantly, individual participant data meta-analyses to reconcile inconsistent findings, identify subgroups most likely to benefit, establish pregnancy-specific CGM thresholds, and evaluate the impact on both obstetric outcomes and long-term postpartum metabolic risk.

Conclusions

This ongoing debate underscores the need to critically appraise the role of CGM in GDM, summarize results from completed RCT’s and meta-analyses, recognize key methodological and clinical evidence gaps, and outline the steps required for CGM to become fully integrated into standard GDM care.

Background

Gestational diabetes mellitus and current treatment approaches

Gestational diabetes mellitus (GDM) is defined as hyperglycaemia first detected during pregnancy, in the absence of overt diabetes [1]. The incidence of GDM is rising in parallel with increasing rates of obesity and type 2 diabetes [2]. The International Diabetes Federation (IDF) estimated the global prevalence of hyperglycaemia during pregnancy at approximately 16% in 2024 [2]. Traditionally, GDM is diagnosed between 24 and 30 weeks of gestation, although screening strategies and diagnostic criteria vary worldwide [3]. More recently, several national guidelines also recommend earlier screening in pregnancy for glucose intolerance, particularly for women at increased risk [4–6]. This shift has been driven by the results of the TOBOGM trial, which included 802 women with risk factors for GDM [7]. The study showed that early screening and treatment improved pregnancy outcomes compared with deferred treatment, most notably through a reduction in neonatal respiratory distress [7].

Management of GDM typically involves a multidisciplinary approach, including dietary counselling, promotion of physical activity, and self-monitoring of blood glucose (SMBG), in line with American Diabetes Association (ADA) recommendations (fasting < 95 mg/dL, 1 h < 140 mg/dL and 2 h < 120 mg/dL) [8, 9] (Fig. 1). When GDM is treated with insulin, recommended ADA targets are similar to those used for pregnant women with pregestational diabetes: fasting: 70–95 mg/dL, 1 h: 110–140 mg/dL, and 2 h: 100–120 mg/dL [10].Women with GDM have a higher risk of several adverse pregnancy outcomes, however with appropriate treatment, pregnancy outcomes are generally favourable [11]. Nevertheless, SMBG is often experienced as burdensome and time-consuming [12]. In addition, a diagnosis of GDM is often associated with stress and information overload, while patients are expected to make substantial lifestyle changes without always clearly perceiving the impact of these changes on their glucose levels [13]. In this context, continuous glucose monitoring (CGM) may offer a more patient-friendly alternative or adjunct to SMBG. By providing real-time and continuous insight into glucose patterns, CGM has the potential to improve patients’ understanding of the relationship between lifestyle and glycaemic control, reduce fingerstick burden, and ultimately enhance both glycaemic outcomes and pregnancy outcomes. This raises the question of whether CGM should play a more prominent role in the management of GDM (Fig. 1).

Fig. 1.

The management of GDM. * Women with a BMI ≥ 30 kg/m² and/or a history of GDM, recommendations follow 2024 Flemish screening guidelines [5]. ADA American Diabetes Association, CGM continuous glucose monitoring; GDM gestational diabetes 1, IPD Individual Participant Data meta-analysis, SMBG self-monitoring of blood glucose, GWG gestational weight gain

Why do randomized controlled trials of CGM in GDM show conflicting results?

Over the past decade, increasing evidence has explored the role of CGM in the management of GDM. Several smaller RCT’s, including the FLAMINGO trial using flash glucose monitoring (FGM), assessed CGM during limited periods of pregnancy [14]. The FLAMINGO trial demonstrated significant improvements in fasting glucose and 2-hour postprandial levels during the first four weeks after GDM diagnosis, without differences in insulin initiation or dosing (approximately 30% in both groups). Although no clear effects were observed on treatment intensification or gestational weight gain (GWG), CGM use was associated with improved dietary behaviour and a reduction in macrosomia [14].

More recently, larger RCTs evaluating real-time CGM (rt-CGM) in women with GDM have yielded conflicting results. The CGM in GDM trial conducted in Portland (USA) randomized 111 women and reported a significant improvement in percentage of time in range (TIRp; 60–140 mg/dL) favouring the CGM group (93 ± 6% vs. 88 ± 14%; p = 0.027) [15]. However, the study was not powered to detect differences in maternal or neonatal outcomes, and SMBG safety checks remained necessary because CGM had not yet received FDA approval at the time [15] (Fig. 2).

Fig. 2.

The larger (ongoing) RCT’s investigating CGM in GDM. GDM gestational diabetes, CGM continuous glucose monitoring, SMBG self-monitoring of blood glucose, TIRp Time in pregnancy specific range, LGA large-for-gestational-age, SGA small-for-gestational-age

Two larger RCTs powered for pregnancy outcomes, DipGluMo and GRACE, further highlight the ongoing debate surrounding CGM use in GDM [16, 17] (Fig. 2). DipGluMo (n = 302) found no significant reduction in its composite neonatal outcome, while TIRp at the end of pregnancy was significantly higher in the SMBG group than in the CGM group (97% vs. 92%; p = 0.02) [16]. Interpretation was further complicated by the recommendation for six SMBG measurements per day in the control group (compared with the ADA recommendation of four per day), as well as the high refusal rates for at least one period of blinded CGM use in the control arm (43% of participants in the control group). Nevertheless, patient satisfaction and preference for CGM were higher compared to SMBG use, supporting a potential role in facilitating self-management rather than clearly improving hard clinical outcomes [16]. The GRACE trial (n = 375) reported a reduction in large-for-gestational-age (LGA) infants with CGM use (4% vs. 10%, p = 0.014) [17]. However, the overall incidence of LGA was lower than anticipated (with similar rates of LGA in the control arm as in women without GDM), while relatively high rates of small-for-gestational-age (SGA) infants were observed in both groups (19% vs. 13%), although not significantly different raising concerns about possible overtreatment or excessively strict glycaemic management. The CGM group has slightly higher TIRp during pregnancy follow-up [95% in the CGM group vs. 93% in the control group (p = 0.026)] [17]. Furthermore, both trials reported higher than expected rates of pharmacological treatment [16, 17]. In DipGluMo, insulin was used in 55% of women in the CGM group compared with 45% int the SMBG group (p = 0.35), while in the GRACE trial, pharmacotherapy (insulin and/or metformin) was initiated in 61% and 55% of participants (p = 0.28), respectively [16, 17]. Notably, none of these larger RCTs specifically evaluated early GDM (diagnosed before 20 weeks), a subgroup associated with a higher risk of adverse pregnancy outcomes and increased insulin requirements [18–20]. This remains an important gap in the literature and raises questions about the potential role of CGM in women with early GDM (Fig. 2).

To date, only a limited number of smaller studies have evaluated CGM in early GDM. The Steady Sugar Trial included 128 women diagnosed with early GDM between 8 and 26 weeks of gestation, with a mean diagnose at 19 weeks for both groups. The trial found no significant improvement in the primary outcome (TIRp 63–140 mg/dL), although several secondary maternal and neonatal outcomes favoured the CGM group: unplanned caesarean Sect.  (20.0% vs. 44.4%, p = 0.046), preterm delivery (6.8% vs. 18.4%, p = 0.041), LGA (5.0% vs. 18.4%, p = 0.019), and neonatal intensive care unit (NICU) admissions (22.5% vs. 44.7%, p = 0.013), together with higher patient satisfaction and reported behavioural benefits, while insulin use remained similar between groups (38% vs. 32%). As suggested by the authors, CGM may improve pregnancy outcomes, without improving TIR, through improved glycaemic awareness and behavioural changes. However, interpretation of the findings is limited because blinded CGM data were incorporated into treatment decisions, reducing the distinction between the CGM and control groups [21].

Importantly, the heterogeneous findings across these RCTs may partly reflect differences in study populations, glycaemic follow-up, pharmacotherapy initiation, and primary outcomes, which complicate direct comparisons between trials and interpretation of the overall clinical benefit of CGM in GDM. Taken together, current evidence from larger RCTs remains inconclusive, highlighting the need for a balanced evaluation of both the potential advantages and limitations of CGM in routine GDM care (Fig. 2). The following sections therefore address these uncertainties by outlining the current advantages and limitations of CGM use in clinical practice, as well as priorities for future research (Fig. 3).

Fig. 3.

Key research gaps for CGM use in GDM. GDM gestational diabetes, CGM continuous glucose monitoring Created with Biorender.com

Main text

Supportive Evidence for CGM use in routine clinical care for GDM

Evidence supporting the use of CGM in GDM is growing, although results from larger RCT’s remain inconsistent. While several studies did not demonstrate clear improvements in pregnancy or glycaemic outcomes, CGM may still offer important benefits in patient engagement, treatment satisfaction, and personalized glucose management.

The benefits of CGM during pregnancy have already been clearly demonstrated in women with type 1 diabetes, most notably in the CONCEPTT trial [22]. Although the pathophysiology of GDM differs from that of type 1 diabetes, similar advantages may apply. CGM provides continuous, real-time glucose data, enabling women with GDM to better understand how dietary patterns influence glucose excursions [14, 23]. Supporting this, a smaller RCT in pregnant women with GDM reported high patients’ satisfaction, with approximately 90% of participants indicating that CGM data influenced diabetes-related decision-making, highlighting the potential of CGM to support more patient-centred care in pregnant women with GDM [23].

CGM may also improve awareness of dietary responses and help reduce the risk of excessive GWG. This is particularly relevant given increasing evidence that women with GDM may benefit from stricter GWG targets than women without GDM [24]. Several smaller studies have supported this finding [25, 26], including an RCT in 110 women with GDM that evaluated the effects of intermittently scanned CGM (is-CGM) [27]. Women using is-CGM were more likely to achieve appropriate GWG by the end of pregnancy compared with controls. In addition, CGM use was associated with improved adherence to SMBG, stricter dietary control, increased engagement in physical activity, more consistent weight monitoring, and better attendance at obstetric check-ups [27]. These findings are in line with the Steady sugar trial, in which the CGM-SAT scores (4.3 ± 0.8, n = 79), based on a 1–5 scale with higher scores reflecting greater satisfaction and perceived benefit, suggested that CGM improved patients’ understanding of how dietary intake and physical activity influence glycaemic control [21].

Clinical Considerations and Limitations of CGM in GDM

While CGM may facilitate more personalized treatment adjustments and reduce the burden of frequent finger-prick testing, robust evidence supporting its routine use in all women with GDM is still conflicting.

A major limitation is the absence of validated GDM specific CGM targets. Although thresholds such as TIRp > 90% and TARp < 10% have been proposed, these are based on expert opinion and have not yet been validated in RCT’s or by large cohort studies evaluating the association between different CGM metrics with adverse pregnancy outcomes across different populations in women with GDM [28]. Consequently, there is currently no agreement on how CGM metrics should be interpreted in clinical practice nor when insulin therapy should be initiated [28]. Since there is no consensus yet, substantial heterogeneity exists in treatment criteria across RCT’s. Most studies initiated insulin therapy according to ADA criteria when at least two measurements within one week had values of ≥ 95 mg/dL fasting or ≥ 140 mg/dL 1-hour postprandial glucose [15–17]. The same targets were used in the SMBG group of the DipGluMo trial, but insulin was initiated when more than 15% of glucose measurements exceeded these targets for over two weeks, whereas in the CGM group insulin was started when TIRp (63–140 mg/dL) was < 85% ¹⁶. The Steady sugar trial used different thresholds, including 1-hour postprandial glucose ≤ 120 mg/dL and fasting glucose ≤ 90 mg/dL ²¹. Furthermore, a secondary analysis of the DiGest trial suggested that a mean CGM glucose target < 110 mg/dL could also be a valuable target, as women who achieved both a mean glucose < 110 mg/dL and ≥ 90% TIRp or < 10% TARp (63–140 mg/dL [3.5–7.8 mmol/L]) at 29 weeks’ gestation had a lower risk of adverse pregnancy outcomes [29]. Taken together, these findings highlight substantial variability in CGM-based treatment thresholds used in GDM and underscore the need for evidence-based targets that are clearly linked to clinically relevant perinatal outcomes [28].

In addition, important questions remain regarding CGM accuracy during pregnancy. CGM measures interstitial glucose, whereas SMBG reflects capillary blood glucose, therefore, a physiological lag-time of approximately 10–15 min may lead to transient discrepancies, especially during rapid glucose changes such as after meals or exercise [30]. In clinical practice, a difference of up to ~ 20% between CGM and SMBG values is generally considered acceptable [30]. Physiological and immunological changes during pregnancy, as well as local inflammatory responses at the sensor-tissue interface, may further affect sensor accuracy and contribute to variability between interstitial and blood glucose values [31, 32]. In addition, device-specific differences, inter-individual variation, and changing physiological conditions throughout gestation complicate the evaluation of CGM accuracy in this population for both CGM and SMBG [30, 33, 34]. However, most commercially available CGM systems have been validated for use during pregnancy [28]. Women with GDM generally use CGM for a shorter duration and are less familiar with the technology compared to type 1 diabetes, which may influence their preferences. While most studies in GDM report a clear preference for CGM over SMBG, these findings may not fully reflect some negative experiences or discontinuation among women with GDM. Reported challenges include sensor inaccuracies, alarms, and data overload, which for some women may contribute to increased stress or reduced adherence [30, 33, 34]. Importantly, stress itself may influence glucose levels and variability, potentially further complicating CGM interpretation [3]. A smaller prospective study with interviews among pregnant women using CGM identified several drawbacks, including sensor defects, practical difficulties, and inconsistencies between CGM readings and SMBG measurements. These discrepancies often led to additional finger-prick testing, which participants described as annoying and reducing the perceived reliability of CGM [35]. Consequently, some women may still prefer SMBG because of its simplicity, familiarity, and lower risk of technical issues.

Evidence for the cost-effectiveness of CGM in GDM remains limited. Compared with SMBG, CGM is more expensive and clinical benefits were not consistent [15–17, 21]. These economic considerations limit the feasibility of routine CGM implementation in GDM, particularly in low-resource settings where the prevalence of GDM is high and access to diabetes technologies remains constrained [36–38]. Even if future studies demonstrate cost-effectiveness, widespread implementation in low-income countries may remain challenging because of financial, infrastructural, and healthcare resource limitations. Future research should therefore include robust cost-effectiveness analyses across diverse healthcare settings to determine which patient populations derive sufficient clinical benefit to justify broader CGM use.

Unanswered Clinical Questions and Future Directions

How should CGM data be translated into future clinical guidelines, and when does CGM add value beyond SMBG in routine GDM care?

Clinical guidelines could be informed by a panel of CGM-derived metrics, including mean glucose and glycaemic variability, to establish an optimal TIRp or other CGM-based targets associated with adverse pregnancy outcomes in GDM. In addition, CGM targets could potentially be adapted across gestation, with trimester-specific or even more granular, week-by-week goals reflecting the rapidly changing metabolic physiology of pregnancy [1]. However, changing gestational targets during different time periods in pregnancy, may also increase the burden on patients and healthcare providers and could negatively affect adherence and implementation. The CONCEPTT trial in pregnant women with type 1 diabetes highlighted the importance of early treatment to reduce pregnancy complications [39]. Although these findings cannot be directly extrapolated to women with GDM, they are consistent with evidence from the TOBOGM trial showing that early pregnancy hyperglycaemia is associated with adverse pregnancy outcomes among women with risk factors for hyperglycaemia [40]. In line with these findings, CGM-based guidelines for GDM will likely need to place greater emphasis on early pregnancy glycaemic management, particularly in the context of expanding early GDM screening strategies. This requires clear clinical pathways from the first trimester onward, supported by structured patient education regarding physiological changes in pregnancy, including the progressive increase in insulin resistance from around 20 weeks of gestation [1]. Such understanding is essential for the correct interpretation of evolving glucose patterns and for guiding timely treatment adjustments throughout pregnancy [30] (Fig. 3).

Furthermore, CGM guidance in GDM should account for high-risk populations, including women with early GDM, obesity, a family history of type 2 diabetes, non-Caucasian ethnicity, or a history of GDM [3]. These subgroups may derive the greatest benefit from CGM-based management strategies. In the DipGluMo study, both obesity and a history of GDM were independently associated with a higher risk of neonatal complications, further underscoring their increased baseline risk and the need for closer glycaemic monitoring and follow-up, with or without CGM [16].

However, current evidence does not yet support routine CGM use in all women with GDM, and robust cost-effectiveness data remain limited. Future research should therefore focus on identifying subgroups most likely to benefit from CGM, including women with early versus late GDM, those requiring pharmacotherapy, and those at higher baseline risk. In parallel, future guidelines should incorporate pragmatic, subgroup-based recommendations that are feasible for both clinicians and patients, rather than a one-size-fits-all approach [34] (Fig. 3).

Can CGM be used as a diagnostic method?

The oral glucose tolerance test (OGTT) remains the standard diagnostic tool for GDM despite several important limitations, including modest reproducibility, low patient acceptability and the absence of universally adopted diagnostic criteria for GDM [3]. In addition, access to OGTT testing is not universally available in low-resource settings [3]. The procedure is burdensome, requiring strict fasting and ingestion of a glucose load that is often poorly tolerated, particularly among women with severe nausea or a history of bariatric surgery, in whom it may provoke dumping syndrome [3, 5, 41]. In women after bariatric surgery, OGTT responses are also characterized by exaggerated early glucose peaks followed by lower baseline and 2-hour glucose values, complicating interpretation [5, 42].

Alternative diagnostic approaches are therefore needed, but currently available biomarkers have important limitations [3]. HbA1c lacks sufficient sensitivity and cannot distinguish early- from late-onset GDM, limiting its utility as a replacement for the OGTT [3]. Nevertheless, the prospective STRiDE cohorts, showed that early HbA1c may be useful for predicting in whom an OGTT could be avoided to screen for GDM later in pregnancy [41]. However, reported risk ratios for the development of GDM did vary across different populations, highlighting the need for population-specific thresholds. Further research is therefore required, particularly given that HbA1c is more widely available and easier to implement in routine clinical practice than the OGTT [41].

The use of CGM for diagnosis and screening for GDM, is being examined. Unlike the OGTT, CGM is well tolerated and can be used safely in all pregnant women, including those unable to tolerate oral glucose loading [3, 5, 42]. It also avoids laboratory-related preanalytical variability and may address some of the reproducibility limitations inherent to the OGTT [34, 43]. The GLAM (glucose levels across maternity) study investigated whether CGM could be used as a diagnostic tool for GDM in an observational cohort of 768 participants, of whom 58 were diagnosed with GDM [44]. Women who developed GDM based on the OGTT later in pregnancy already showed at 13–14 weeks gestation higher CGM-derived mean glucose levels (109 ± 13 vs. 100 ± 8 mg/dL), and higher median TAR > 120 mg/dL (32% vs. 14%) and > 140 mg/dL (5.2% vs. 2.0%). CGM metrics showed moderate predictive ability for GDM, with AUROC values of 0.74 at 13–14 weeks of gestation and 0.81 using second trimester data for % time > 140 mg/dL. These findings suggests that CGM detects dysglycemia weeks to months before OGTT diagnosis, supporting its potential role in earlier identification of women at risk for GDM [44].

The recent completed Maternal Glucose in Pregnancy (MAGIC) study, which is not yet published, assessed intermittent use of CGM in 500 pregnant women from the UK, with diagnosis of GDM based on OGTT using the NICE criteria, and will offer data starting from early pregnancy until two weeks postpartum [43] .

What evidence is needed to address these questions?

The substantial differences in outcome selection, definitions, and methodological approaches among studies evaluating the impact of CGM in women with GDM, limit direct comparisons and the ability to draw definitive conclusions across diverse patient populations. Consequently, it remains unclear which women benefit most from CGM, at what stage of pregnancy, and for which maternal, neonatal, and/or psychosocial outcomes (Fig. 3). Future research should therefore not only evaluate changes in CGM-derived metrics, but also determine which outcomes best reflect clinically meaningful benefits for women with GDM and their offspring.

Several ongoing trials are expected to provide important evidence. The CORDELIA trial (NCT06310356) is a multicentre international RCT evaluating the clinical effectiveness, cost-effectiveness, and patient reported outcomes of CGM use in 386 women with GDM, of whom at least 30% have early GDM. Recruitment was completed in June 2026. The IMAGINE trial (NCT06957028), another ongoing multicentre international trial, investigates the use of CGM for the detection and management of hyperglycaemia in early pregnancy in approximately 6000 pregnant women. In addition, the RECOGNISED trial will evaluate CGM use in 2116 women diagnosed with GDM, further strengthening the evidence base for CGM implementation in pregnancy [45].

Although these large prospective trials are essential, individual trials may still have limited power to evaluate clinically relevant subgroups or uncommon outcomes. Therefore, collaboration and data harmonization across studies remains important. By analysing large datasets at the individual participant level, IPD-meta-analysis enables harmonization of outcomes, consistent adjustment for confounding factors, and sufficiently powered analyses of clinically relevant subgroups. This approach may provide more robust evidence on the effects of CGM on glycaemic control, pregnancy outcomes, participant-reported outcomes, and postpartum metabolic risk in women with GDM. To facilitate this, the ELYSA consortium, coordinated by our research group, was established as an international collaborative platform bringing together investigators from RCTs and observational cohort studies evaluating CGM in GDM. The consortium is organized around four initial predefined research questions: (1) the effect of CGM on glycaemic and pregnancy outcomes using RCT data, (2) associations between CGM metrics and adverse pregnancy outcomes using RCT and cohort data, (3) the relationship between CGM profiles and postpartum glucose intolerance, and (4) the use of machine-learning approaches to identify novel glycaemic metrics associated with adverse outcomes (Fig. 3). Through secure and ethically approved data-sharing frameworks, participating investigators remain actively involved in the scientific process, data interpretation, and dissemination. Nevertheless, the IPD approach also has important practical limitations. Data-sharing restrictions may prevent inclusion of all eligible datasets, introducing potential availability bias. To help mitigate this risk, the ELYSA consortium held an initial meeting with investigators from the largest eligible studies to assess interest in collaboration. Nevertheless, by harmonizing individual-level data across studies, ELYSA aims to generate high-quality evidence to support evidence-based integration of CGM into routine care for women with GDM. Detailed protocols, including the statistical analysis plans for each predefined research question, were developed in collaboration with an experienced biostatistician and an expert in IPD meta-analysis methodology and are available upon reasonable request to interested collaborators. Further information and opportunities for collaboration are available at https://www.elysaconsortiumongestationaldiabetes.org/.

Conclusions

Recent large RCTs evaluating CGM use in GDM have shown conflicting results, and uncertainty remains regarding its overall clinical benefit and the populations most likely to benefit. Future research should therefore focus on large collaborative initiatives, including IPD meta-analyses, to harmonize outcomes, reduce methodological heterogeneity, and support a more individualized and evidence-based integration of CGM into routine GDM care.

Abbreviations

ADA

American Diabetes Association

AUROC

Area Under the Receiver Operating Characteristic Curve

CGM

Continuous Glucose Monitoring

CGM

SAT–Continuous Glucose Monitoring Satisfaction Scale

CORDELIA

Continuous Glucose Monitoring for Women With Gestational Diabetes

DiGest

Dietary Intervention in Pregnant Women with Gestational Diabetes

DipGluMo

Continuous glucose monitoring in the management of gestational diabetes in Switzerland

ELYSA

International consortium for individual participant data meta–analyses in gestational diabetes

FDA

Food and Drug Administration

FGM

Flash Glucose Monitoring

GLAM

Glucose Levels Across Maternity

GDM

Gestational Diabetes Mellitus

GRACE

Glycaemic control and pregnancy outcomes with real–time continuous glucose monitoring in gestational diabetes

GWG

Gestational Weight Gain

HbA1c

Glycated Hemoglobin A1c

IDF

International Diabetes Federation

IMAGINE

CGM for the early detection and management of hyperglycemia in pregnancy

IPD

Individual Participant Data

is

CGM–Intermittently Scanned Continuous Glucose Monitoring

LGA

Large for Gestational Age

MAGIC

Maternal Glucose in Pregnancy

NICU

Neonatal Intensive Care Unit

NICE

National Institute for Health and Care Excellence

OGTT

Oral Glucose Tolerance Test

RCT

Randomized Controlled Trial

RECOGNISED

randomised controlled trial of continuous Glucose monitoring in the management and diagnosis of gestational Diabetes Mellitus

rt

CGM–Real–Time Continuous Glucose Monitoring

SGA

Small for Gestational Age

SMBG

Self–Monitoring of Blood Glucose

TARp

Time Above Range in Pregnancy

TIRp

Time in Range in Pregnancy

TOBOGM

Treatment of Booking Gestational Diabetes Mellitus

Author contributions

I.G, C.Y, N.E and K.B contributed to writing the first draft of the manuscript. I.G and N.E contributed to figures and tables. All authors read and approved the final manuscript.

Funding

No funding was received for this manuscript.

Data availability

All reported data in this debate has been cited and sourced from the original publication.

Declarations

Ethics approval and consent to participate

not applicable.

Consent for publication

not applicable.

AI statement

Microsoft Copilot was used solely for language editing to improve the clarity and readability of the manuscript. The authors reviewed and edited all AI-assisted text and take full responsibility for the content of the manuscript.

Competing interests

I.G. is the recipient of a PhD fellowship, strategic basic research (FWO-SB) from the Flemish Research Council (FWO) 1S82826N. KB received a senior clinical fellowship from FWO Flanders (1800220 N). KB received an unrestricted grant and study devices from Medtronic for the investigator-initiated CRISTAL study. KB received study devices from Dexcom for the investigator-initiated GLORIA study. KB received study medication from Novo Nordisk A/S for the investigator-initiated SERENA study. KB received study devices from Abbott and LifeScan, and a grant from Abbott for the investigator-initiated CORDELIA study. KB received a speaker’s fee from Novo Nordisk, AstraZeneca, and Medtronic. KB was a Guest Editor for the Diagnosis and Management of Gestational Diabetes collection (special issue on gestational diabetes). All other authors declared no competing interests.